├── Offload_Strategy.py ├── README.md ├── RL_DDQN.py ├── RL_DQN.py ├── RL_DuelingDQN.py ├── RL_PRDQN.py ├── dataset ├── edgeResources-melbCBD.csv └── users-melbcbd-generated.csv ├── main.py ├── results ├── DuelingDQN10loss.png ├── DuelingDQN10loss.svg └── _DuelingDQN10.csv ├── sysModel.py ├── userNum ├── ACTION_DuelingDQN10_mec0_2_50.data ├── ACTION_DuelingDQN10_mec1_4_50.data ├── ACTION_DuelingDQN10_mec2_6_50.data ├── ACTION_DuelingDQN10_mec3_8_50.data ├── ACTION_PRDQN10_mec0_2_50.data ├── ACTION_PRDQN10_mec1_4_50.data ├── ACTION_PRDQN10_mec2_6_50.data ├── ACTION_PRDQN10_mec3_8_50.data ├── ACTION_offline3_10_mec0_2_50.data ├── ACTION_offline3_10_mec1_4_50.data ├── ACTION_offline3_10_mec2_6_50.data ├── ACTION_offline3_10_mec3_8_50.data ├── ACTION_online3_10_mec0_2_50.data ├── ACTION_online3_10_mec1_4_50.data ├── ACTION_online3_10_mec2_6_50.data ├── ACTION_online3_10_mec3_8_50.data ├── JOB_DuelingDQN10_mec0_250.data ├── JOB_DuelingDQN10_mec0_450.data ├── JOB_DuelingDQN10_mec0_650.data ├── JOB_DuelingDQN10_mec0_850.data ├── JOB_PRDQN10_mec0_250.data ├── JOB_PRDQN10_mec0_450.data ├── JOB_PRDQN10_mec0_650.data ├── JOB_PRDQN10_mec0_850.data ├── JOB_offline3_10_rho_2.data ├── JOB_offline3_10_rho_4.data ├── JOB_offline3_10_rho_6.data ├── JOB_offline3_10_rho_8.data ├── JOB_online3_10_rho_2.data ├── JOB_online3_10_rho_4.data ├── JOB_online3_10_rho_6.data ├── JOB_online3_10_rho_8.data ├── USER_DuelingDQN10_mec0_250.data ├── USER_DuelingDQN10_mec1_450.data ├── USER_DuelingDQN10_mec2_650.data ├── USER_DuelingDQN10_mec3_850.data ├── USER_PRDQN10_mec0_250.data ├── USER_PRDQN10_mec1_450.data ├── USER_PRDQN10_mec2_650.data ├── USER_PRDQN10_mec3_850.data ├── USER_offline3_10_mec0_250.data ├── USER_offline3_10_mec1_450.data ├── USER_offline3_10_mec2_650.data ├── USER_offline3_10_mec3_850.data ├── USER_online3_10_mec0_250.data ├── USER_online3_10_mec1_450.data ├── USER_online3_10_mec2_650.data └── USER_online3_10_mec3_850.data └── usermove ├── user_0.data ├── user_1.data ├── user_2.data ├── user_3.data ├── user_4.data ├── user_5.data ├── user_6.data ├── user_7.data ├── user_8.data └── user_9.data /Offload_Strategy.py: -------------------------------------------------------------------------------- 1 | """ 2 | offloading strategy 3 | """ 4 | import sysModel 5 | # from RL_DQN import DQN 6 | # from RL_DDQN import DDQN 7 | from RL_DuelingDQN import DuelingDQN 8 | # from RL_PRDQN import PRDQN 9 | import simpy 10 | import random 11 | import xlwt 12 | import numpy as np 13 | 14 | userNum = 10 15 | mecNum = 4 16 | pen = 1 17 | # name = 'DQN' 18 | # name = 'DDQN' 19 | name = 'DuelingDQN' 20 | # name = 'PRDQN' 21 | simTime = 800 22 | 23 | LEPI = 100 # episode 24 | jobDuration= 30 25 | 26 | 27 | 28 | def other_job(env, repairman): 29 | while True: 30 | done_in = jobDuration 31 | while done_in: 32 | with repairman.request(priority=2) as req: 33 | yield req 34 | try: 35 | start = env.now 36 | yield env.timeout(done_in) 37 | done_in = 0 38 | except simpy.Interrupt: 39 | done_in -= env.now - start 40 | 41 | 42 | 43 | class OFFLOAD(object): 44 | def __init__(self): 45 | self.name = name 46 | self.action_space = [str(i) for i in range(2)] 47 | self.n_actions = userNum # 0-local，1-offload 48 | self.n_features = 7 # system state - getstate 49 | self.RL = DuelingDQN(self.n_actions, self.n_features, 50 | learning_rate=0.03, 51 | reward_decay=0.4, 52 | e_greedy=0.9, # exploration 53 | replace_target_iter=200, 54 | memory_size=20000, 55 | # output_graph=True 56 | ) 57 | self.done = True 58 | self.stepcount = 0 59 | 60 | def reset(self): 61 | self.done = True 62 | self.stepcount = 0 63 | 64 | def printLost(self): 65 | self.RL.plot_lost(self.name, self.n_actions) 66 | 67 | def refreshstep(self, mec_, env_): 68 | while True: 69 | yield env_.timeout(mec_.TIMER) 70 | mec_.sysTime = env_.now 71 | jobpool = [] 72 | for Jr in mec_.jobPool: 73 | userID = Jr[0] 74 | jobID = Jr[1] 75 | onejob = mec_.ul.userList[userID].jobList[jobID] 76 | 77 | if onejob.jobRunLeft > mec_.TIMER: 78 | jobpool.append((userID, jobID)) 79 | mec_.ul.userList[userID].jobList[jobID].jobRunLeft = mec_.ul.userList[userID].jobList[ 80 | jobID].jobRunLeft - mec_.TIMER 81 | else: 82 | mec_.SYS_CPU -= (mec_.ul.userList[userID].jobCPU / mec_.RHO) 83 | mec_.ul.userList[userID].jobrefresh(env_, mec_.ul.userList[userID].jobList[jobID]) 84 | mec_.offloadJob.append(mec_.ul.userList[userID].jobList[jobID]) 85 | 86 | mec_.ul.userList[userID].jobList[jobID].jobAge = env_.now - mec_.ul.userList[userID].jobList[ 87 | jobID].jobAge 88 | mec_.Age += mec_.ul.userList[userID].jobList[jobID].jobAge 89 | mec_.Run += mec_.ul.userList[userID].jobList[jobID].jobRun 90 | mec_.commTime += mec_.ul.userList[userID].jobList[jobID].jobTT 91 | mec_.Throughout += 1 92 | 93 | failrate = mec_.Failure / mec_.Throughout 94 | 95 | score = mec_.ul.userList[userID].jobList[jobID].jobRun / mec_.ul.userList[userID].jobList[ 96 | jobID].jobCEnergy 97 | 98 | if (mec_.ul.userList[userID].jobList[jobID].jobAge > mec_.ul.userList[userID].jobList[ 99 | jobID].jobRun): 100 | mec_.SCORE = -abs(score) * (1 - failrate) 101 | mec_.ul.userList[userID].jobList[jobID].jobState = 'FL' 102 | mec_.Failure += 1 103 | else: 104 | mec_.SCORE = score * (1 - failrate) 105 | mec_.ul.userList[userID].jobList[jobID].jobState = 'CP' 106 | mec_.REWARD += mec_.SCORE 107 | 108 | mec_.priorityList[userID] = mec_.ACTION 109 | mec_.ul.userList[userID].userPriority[mec_.ID] = mec_.ACTION 110 | 111 | 112 | mec_.jobPool = jobpool 113 | 114 | def step(self, mec_, env_): 115 | counter = 0 116 | while True: 117 | counter += 1 118 | yield env_.timeout(mec_.TIMER) 119 | 120 | if (mec_.CHANNEL - mec_.channelUsed <= 1) or (mec_.sysCPU > 0.9): 121 | mec_.SCORE = -abs(mec_.SCORE) 122 | yield env_.timeout(mec_.TIMER * mec_.Delta) 123 | continue 124 | else: 125 | observation = mec_.getstate() 126 | mec_.ACTION = self.RL.choose_action(observation) 127 | if (counter < userNum * 5) or (counter % 10 == 0): 128 | pkey = random.sample([i for i in range(userNum)], k=mec_.ACTION) 129 | else: 130 | plist = sorted(mec_.priorityList.items(), key=lambda i: i[1], reverse=True) 131 | pkey = [plist[i][0] for i in range(len(plist))][:mec_.ACTION] 132 | for i in range(len(pkey)): 133 | userID = pkey[i] 134 | mec_.offloadOne(env_, userID) 135 | observation_ = mec_.getstate() 136 | reward = mec_.SCORE 137 | 138 | self.RL.store_transition(observation, mec_.ACTION, reward, observation_) 139 | if (self.stepcount > 40) and (self.stepcount % 4 == 0): 140 | self.RL.learn() 141 | observation = observation_ 142 | self.stepcount += 1 143 | 144 | def update(self, ql,RDSEED, uLng, uLat, sLng, sLat, 145 | rList): 146 | self.reset() 147 | for episode in range(LEPI): 148 | self.reset() 149 | print("episode %d step count %d" % (episode, self.stepcount)) 150 | random.seed(RDSEED) 151 | ul = sysModel.UL() 152 | 153 | for i in range(ul.userNum): 154 | user = sysModel.User(i) 155 | user.usersetting(uLng[i], uLat[i]) 156 | user.usercreat() 157 | ul.userList.append(user) 158 | 159 | mec_l = sysModel.MEC_L() 160 | rho = 2 161 | for j in range(mec_l.mecNum): 162 | mec = sysModel.MEC(j, ul) 163 | mec.setMEC(sLng[j], sLat[j], rho + j * 2, 50, rList[j]) 164 | mec_l.mecList.append(mec) 165 | 166 | env = simpy.Environment() 167 | repairman = simpy.PreemptiveResource(env, capacity=1) 168 | 169 | env.process(ul.mobile(env, mec_l)) # user mobile 170 | 171 | edgeservers = [sysModel.SIMRUN(env, i, ql,repairman) 172 | for i in mec_l.mecList] 173 | env.process(other_job(env,repairman)) 174 | env.run(until=simTime) 175 | self.reset() 176 | 177 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | This is the code for unpublished papers. Note:(The title of the paper will be attached afterwards) 2 | # Installation 3 | _____ 4 | - Python 3.8 5 | - SimPy, You can install SimPy easily via pip _: 6 | ```sh 7 | $ pip install -U simpy 8 | ``` 9 | - tensoflow 1 version 10 | 11 | # launch 12 | ____ 13 | The startup program simply runs the main function directly, then adjusts the number of edge servers and users in the participating environment by changing the global variable parameters,such as simulation time `simeTime`, the processing speed of edge server `rho`, Edge server cache pool `buffer`, the cache pool indicates that the server reads unloaded tasks from the queue pool. The implement of offloading algorithm by changing different `name` in Offloading_Strategy.py file. 14 | The simulation has two parts, one is `Simulation_NONRL` without reinforcement learning, and the other is `Simulation_RL` with reinforcement learning.The fault module uses SimPy discrete event simulation to represent the interruption of the edge server. When the server resources fail instantaneously, an interval of event recovery occurs. After recovery, priority is given to the tasks performed before the interruption. 15 | 16 | # Code structure 17 | ______ 18 | - `./dataset` :contains edgeResources-melbCBD.csv and users-melbdbd-generated.csv files from [EUA datasets](https://github.com/PhuLai/eua-dataset). 19 | - `./results`: Dueling DQN algorithm results 20 | - `./userName`: Recording the results of different reinforcement learning algorithms on different edge servers with different users 21 | - `./usermove`: Recording the latitude and longitude of random movements of users 22 | - `Offloading_Strategy.py`: repsents the offloading strategy of user 23 | - `RL_DDQN.py`:represents the Double Deep-Q Network algorithm 24 | - `RL_DQN.py`:represents the Deep Q network alogrithm 25 | - `RL_Dueling DQN.py`: represents the Dueling Deep Q network algorithm 26 | - `RL_PRDQN.py`: represents the Deep Q network with the priority experience replay 27 | - `main.py`: task offloading main function with fault tolerance 28 | - `sysMpdel.py`:Includes task generation, user movement, edge server resources, edge server fault recovery. 29 | -------------------------------------------------------------------------------- /RL_DDQN.py: -------------------------------------------------------------------------------- 1 | """ 2 | The double DQN 3 | Using Tensorflow: 1.0 4 | """ 5 | 6 | import numpy as np 7 | import tensorflow.compat.v1 as tf 8 | tf.disable_v2_behavior() 9 | import matplotlib.pyplot as plt 10 | import pandas as pd 11 | np.random.seed(15) 12 | 13 | class DDQN: 14 | def __init__( 15 | self, 16 | n_actions, 17 | n_features, 18 | learning_rate=0.005, 19 | reward_decay=0.9, 20 | e_greedy=0.9, 21 | replace_target_iter=200, 22 | memory_size=3000, 23 | batch_size=32, 24 | e_greedy_increment=None, 25 | output_graph=False, 26 | double_q=True, 27 | sess=None, 28 | ): 29 | self.n_actions = n_actions 30 | self.n_features = n_features 31 | self.lr = learning_rate 32 | self.gamma = reward_decay 33 | self.epsilon_max = e_greedy 34 | self.replace_target_iter = replace_target_iter 35 | self.memory_size = memory_size 36 | self.batch_size = batch_size 37 | self.epsilon_increment = e_greedy_increment 38 | self.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max 39 | 40 | self.double_q = double_q # decide to use double q or not 41 | 42 | self.learn_step_counter = 0 43 | self.memory = np.zeros((self.memory_size, n_features*2+2)) 44 | self._build_net() 45 | e_params = tf.get_collection('eval_net_params') 46 | t_params = tf.get_collection('target_net_params') 47 | self.replace_target_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)] 48 | 49 | if sess is None: 50 | self.sess = tf.Session() 51 | self.sess.run(tf.global_variables_initializer()) 52 | else: 53 | self.sess = sess 54 | if output_graph: 55 | tf.summary.FileWriter("logs/", self.sess.graph) 56 | self.loss_his = [] 57 | 58 | def _build_net(self): 59 | def build_layers(s, c_names, n_l1, n_l2, w_initializer, b_initializer): 60 | with tf.variable_scope('l1'): 61 | w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names) 62 | b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names) 63 | l1 = tf.nn.relu(tf.matmul(s, w1) + b1) 64 | with tf.variable_scope('h1'): 65 | wh1 = tf.get_variable('wh1', [n_l1, 100], initializer=w_initializer, collections=c_names) 66 | bh1 = tf.get_variable('bh1', [1, 100], initializer=b_initializer, collections=c_names) 67 | lh1 = tf.nn.relu(tf.matmul(l1, wh1) + bh1) 68 | 69 | with tf.variable_scope('h2'): 70 | wh2 = tf.get_variable('wh2', [100, n_l2], initializer=w_initializer, collections=c_names) 71 | bh2 = tf.get_variable('bh2', [1, n_l2], initializer=b_initializer, collections=c_names) 72 | lh2 = tf.nn.relu(tf.matmul(lh1, wh2) + bh2) 73 | 74 | with tf.variable_scope('l2'): 75 | w2 = tf.get_variable('w2', [n_l2, self.n_actions], initializer=w_initializer, collections=c_names) 76 | b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names) 77 | out = tf.matmul(lh2, w2) + b2 78 | return out 79 | 80 | # ------------------ build evaluate_net ------------------ 81 | self.s = tf.placeholder(tf.float32, [None, self.n_features], name='s') # input 82 | self.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name='Q_target') # for calculating loss 83 | 84 | with tf.variable_scope('eval_net'): 85 | c_names, n_l1, n_l2, w_initializer, b_initializer = \ 86 | ['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES], 20,20, \ 87 | tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1) # config of layers 88 | 89 | self.q_eval = build_layers(self.s, c_names, n_l1, n_l2, w_initializer, b_initializer) 90 | 91 | with tf.variable_scope('loss'): 92 | self.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval)) 93 | with tf.variable_scope('train'): 94 | self._train_op = tf.train.AdamOptimizer(self.lr).minimize(self.loss) 95 | 96 | # ------------------ build target_net ------------------ 97 | self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_') # input 98 | with tf.variable_scope('target_net'): 99 | c_names = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES] 100 | 101 | self.q_next = build_layers(self.s_, c_names, n_l1, n_l2, w_initializer, b_initializer) 102 | 103 | def store_transition(self, s, a, r, s_): 104 | if not hasattr(self, 'memory_counter'): 105 | self.memory_counter = 0 106 | transition = np.hstack((s, [a, r], s_)) 107 | index = self.memory_counter % self.memory_size 108 | self.memory[index, :] = transition 109 | self.memory_counter += 1 110 | 111 | def choose_action(self, observation): 112 | observation = observation[np.newaxis, :] 113 | actions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation}) 114 | action = np.argmax(actions_value) 115 | 116 | if not hasattr(self, 'q'): # record action value it gets 117 | self.q = [] 118 | self.running_q = 0 119 | self.running_q = self.running_q * 0.99 + 0.01 * np.max(actions_value) 120 | self.q.append(self.running_q) 121 | 122 | if np.random.uniform() > self.epsilon: # choosing action 123 | action = np.random.randint(0, self.n_actions) 124 | return action 125 | 126 | def learn(self): 127 | if self.learn_step_counter % self.replace_target_iter == 0: 128 | self.sess.run(self.replace_target_op) 129 | print('\ntarget_params_replaced\n') 130 | 131 | if self.memory_counter > self.memory_size: 132 | sample_index = np.random.choice(self.memory_size, size=self.batch_size) 133 | else: 134 | sample_index = np.random.choice(self.memory_counter, size=self.batch_size) 135 | batch_memory = self.memory[sample_index, :] 136 | 137 | q_next, q_eval4next = self.sess.run( 138 | [self.q_next, self.q_eval], 139 | feed_dict={self.s_: batch_memory[:, -self.n_features:], # next observation 140 | self.s: batch_memory[:, -self.n_features:]}) # next observation 141 | q_eval = self.sess.run(self.q_eval, {self.s: batch_memory[:, :self.n_features]}) 142 | 143 | q_target = q_eval.copy() 144 | 145 | batch_index = np.arange(self.batch_size, dtype=np.int32) 146 | eval_act_index = batch_memory[:, self.n_features].astype(int) 147 | reward = batch_memory[:, self.n_features + 1] 148 | 149 | if self.double_q: 150 | max_act4next = np.argmax(q_eval4next, 151 | axis=1) # the action that brings the highest value is evaluated by q_eval 152 | selected_q_next = q_next[batch_index, max_act4next] # Double DQN, select q_next depending on above actions 153 | else: 154 | selected_q_next = np.max(q_next, axis=1) # the natural DQN 155 | 156 | q_target[batch_index, eval_act_index] = reward + self.gamma * selected_q_next 157 | 158 | _, self.loss_ = self.sess.run([self._train_op, self.loss], 159 | feed_dict={self.s: batch_memory[:, :self.n_features], 160 | self.q_target: q_target}) 161 | self.loss_his.append(self.loss_) 162 | 163 | self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max 164 | self.learn_step_counter += 1 165 | 166 | # 在这里设置输出的文件 167 | def plot_lost(self, name='DDQN', userNum='5'): 168 | data = pd.DataFrame(self.loss_his, 169 | index=np.arange(len(self.loss_his)), 170 | columns=['loss']) 171 | data.to_csv('./results/' + 172 | '_' + name + str(userNum) + '.csv') 173 | 174 | plt.plot(np.arange(len(self.loss_his)), self.loss_his) 175 | plt.ylabel('Loss') 176 | plt.xlabel('training steps') 177 | plt.savefig('./results/' + 178 | name + str(userNum) + 'loss.svg', format='svg', dpi=400) 179 | plt.savefig('./results/' + 180 | name + str(userNum) + 'loss.png', format='png', dpi=400) 181 | plt.show() -------------------------------------------------------------------------------- /RL_DQN.py: -------------------------------------------------------------------------------- 1 | """ 2 | Deep Q Network offloading strategy 3 | running environment is Tensorfow 1.0 4 | """ 5 | import numpy as np 6 | import tensorflow.compat.v1 as tf 7 | tf.disable_v2_behavior() 8 | import matplotlib.pyplot as plt 9 | import pandas as pd 10 | 11 | np.random.seed(15) 12 | 13 | class DQN: 14 | def __init__( 15 | self, 16 | n_actions, 17 | n_features, 18 | learning_rate=0.01, 19 | reward_decay=0.9, 20 | e_greedy=0.9, 21 | replace_target_iter=100, 22 | memory_size=500, 23 | batch_size=32, 24 | e_greedy_increment=None, 25 | output_graph=False, 26 | ): 27 | self.n_actions = n_actions 28 | self.n_features = n_features 29 | self.lr = learning_rate 30 | self.gamma = reward_decay 31 | self.epsilon_max = e_greedy 32 | self.replace_target_iter = replace_target_iter 33 | self.memory_size = memory_size 34 | self.batch_size = batch_size 35 | self.epsilon_increment = e_greedy_increment 36 | self.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max 37 | 38 | # total learning step 39 | self.learn_step_counter = 0 40 | 41 | # initialize zero memory [s, a, r, s_] 42 | self.memory = np.zeros((self.memory_size, n_features * 2 + 2)) 43 | 44 | # consist of [target_net, evaluate_net] 45 | self._build_net() 46 | e_params = tf.get_collection('eval_net_params') 47 | t_params = tf.get_collection('target_net_params') 48 | 49 | # Parameter substitution 50 | self.replace_target_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)] 51 | 52 | self.sess = tf.Session() 53 | 54 | if output_graph: 55 | tf.summary.FileWriter("logs/", self.sess.graph) 56 | 57 | self.sess.run(tf.global_variables_initializer()) 58 | self.loss_his = [] # historical loss value 59 | 60 | def _build_net(self): 61 | # ------------------ build evaluate_net ------------------ 62 | self.s = tf.placeholder(tf.float32, [None, self.n_features], name='s') # input 63 | self.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name='Q_target') # for calculating loss 64 | with tf.variable_scope('eval_net'): 65 | # c_names(collections_names) are the collections to store variables 66 | c_names, n_l1, n_l2, w_initializer, b_initializer = \ 67 | ['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES], 20, 20, \ 68 | tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1) # config of layers 69 | 70 | # first layer. collections is used later when assign to target net 71 | with tf.variable_scope('l1'): 72 | w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names) 73 | b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names) 74 | l1 = tf.nn.relu(tf.matmul(self.s, w1) + b1) 75 | 76 | # hidden layer1. collections is used later when assign to target net 77 | with tf.variable_scope('lm1'): 78 | wm1 = tf.get_variable('wm1', [n_l1, 100], initializer=w_initializer, collections=c_names) 79 | bm1 = tf.get_variable('bm1', [1, 100], initializer=b_initializer, collections=c_names) 80 | lm1 = tf.nn.relu(tf.matmul(l1, wm1) + bm1) 81 | 82 | # hidden layer2. collections is used later when assign to target net 83 | with tf.variable_scope('lm2'): 84 | wm2 = tf.get_variable('wm2', [100, n_l2], initializer=w_initializer, collections=c_names) 85 | bm2 = tf.get_variable('bm2', [1, n_l2], initializer=b_initializer, collections=c_names) 86 | lm2 = tf.nn.relu(tf.matmul(lm1, wm2) + bm2) 87 | 88 | # final layer. collections is used later when assign to target net 89 | with tf.variable_scope('l2'): 90 | w2 = tf.get_variable('w2', [n_l2, self.n_actions], initializer=w_initializer, collections=c_names) 91 | b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names) 92 | self.q_eval = tf.matmul(lm2, w2) + b2 93 | 94 | with tf.variable_scope('loss'): 95 | self.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval)) 96 | with tf.variable_scope('train'): 97 | self._train_op = tf.train.AdamOptimizer(self.lr).minimize(self.loss) 98 | 99 | # ------------------ build target_net ------------------ 100 | self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_') # input 101 | with tf.variable_scope('target_net'): 102 | # c_names(collections_names) are the collections to store variables 103 | c_names = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES] 104 | 105 | # first layer. collections is used later when assign to target net 106 | with tf.variable_scope('l1'): 107 | w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names) 108 | b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names) 109 | l1 = tf.nn.relu(tf.matmul(self.s_, w1) + b1) 110 | 111 | # hidden layer1. collections is used later when assign to target net 112 | with tf.variable_scope('lm1'): 113 | wm1 = tf.get_variable('wm1', [n_l1, 100], initializer=w_initializer, collections=c_names) 114 | bm1 = tf.get_variable('bm1', [1, 100], initializer=b_initializer, collections=c_names) 115 | lm1 = tf.nn.relu(tf.matmul(l1, wm1) + bm1) 116 | 117 | # hidden layer2. collections is used later when assign to target net 118 | with tf.variable_scope('lm2'): 119 | wm2 = tf.get_variable('wm2', [100, n_l2], initializer=w_initializer, collections=c_names) 120 | bm2 = tf.get_variable('bm2', [1, n_l2], initializer=b_initializer, collections=c_names) 121 | lm2 = tf.nn.relu(tf.matmul(lm1, wm2) + bm2) 122 | 123 | # final layer. collections is used later when assign to target net 124 | with tf.variable_scope('l2'): 125 | w2 = tf.get_variable('w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names) 126 | b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names) 127 | self.q_next = tf.matmul(lm2, w2) + b2 128 | 129 | def store_transition(self, s, a, r, s_): 130 | if not hasattr(self, 'memory_counter'): 131 | self.memory_counter = 0 132 | 133 | transition = np.hstack((s, [a, r], s_)) 134 | 135 | # replace the old memory with new memory 136 | index = self.memory_counter % self.memory_size 137 | self.memory[index, :] = transition 138 | 139 | self.memory_counter += 1 140 | 141 | def choose_action(self, observation): 142 | # to have batch dimension when feed into tf placeholder 143 | observation = observation[np.newaxis, :] 144 | 145 | if np.random.uniform() < self.epsilon: 146 | # forward feed the observation and get q value for every actions 147 | actions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation}) 148 | action = np.argmax(actions_value) 149 | else: 150 | action = np.random.randint(0, self.n_actions) 151 | return action 152 | 153 | def learn(self): 154 | # check to replace target parameters 155 | if self.learn_step_counter % self.replace_target_iter == 0: 156 | self.sess.run(self.replace_target_op) 157 | print('\ntarget_params_replaced\n') 158 | 159 | # sample batch memory from all memory 160 | if self.memory_counter > self.memory_size: 161 | sample_index = np.random.choice(self.memory_size, size=self.batch_size) 162 | else: 163 | sample_index = np.random.choice(self.memory_counter, size=self.batch_size) 164 | batch_memory = self.memory[sample_index, :] 165 | 166 | q_next, q_eval = self.sess.run( 167 | [self.q_next, self.q_eval], 168 | feed_dict={ 169 | self.s_: batch_memory[:, -self.n_features:], # fixed params 170 | self.s: batch_memory[:, :self.n_features], # newest params 171 | }) 172 | 173 | # change q_target w.r.t q_eval's action 174 | q_target = q_eval.copy() 175 | 176 | batch_index = np.arange(self.batch_size, dtype=np.int32) 177 | eval_act_index = batch_memory[:, self.n_features].astype(int) 178 | reward = batch_memory[:, self.n_features + 1] 179 | 180 | q_target[batch_index, eval_act_index] = reward + self.gamma * np.max(q_next, axis=1) 181 | 182 | _, self.loss_ = self.sess.run([self._train_op, self.loss], 183 | feed_dict={self.s: batch_memory[:, :self.n_features], 184 | self.q_target: q_target}) 185 | self.loss_his.append(self.loss_) 186 | 187 | # increasing epsilon 188 | self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max 189 | self.learn_step_counter += 1 190 | 191 | def plot_lost(self, name='DQN', userNum='5'): 192 | data = pd.DataFrame(self.loss_his, 193 | index=np.arange(len(self.loss_his)), 194 | columns=['loss']) 195 | data.to_csv('./results/' + 196 | '_' + name + str(userNum) + '.csv') 197 | 198 | plt.plot(np.arange(len(self.loss_his)), self.loss_his) 199 | plt.ylabel('Loss') 200 | plt.xlabel('training steps') 201 | plt.savefig('./results/' + 202 | name + str(userNum) + 'loss.svg', format='svg', dpi=400) 203 | plt.savefig('./results/' + 204 | name + str(userNum) + 'loss.png', format='png', dpi=400) 205 | plt.show() 206 | -------------------------------------------------------------------------------- /RL_DuelingDQN.py: -------------------------------------------------------------------------------- 1 | """ 2 | The Dueling DQN 3 | """ 4 | 5 | import numpy as np 6 | import pandas as pd 7 | import matplotlib.pyplot as plt 8 | import tensorflow.compat.v1 as tf 9 | tf.disable_v2_behavior() 10 | 11 | np.random.seed(15) 12 | # tf.set_random_seed(1) 13 | 14 | 15 | class DuelingDQN: 16 | def __init__( 17 | self, 18 | n_actions, 19 | n_features, 20 | learning_rate=0.1, # initial value =0.001 21 | reward_decay=0.9, 22 | e_greedy=0.9, 23 | replace_target_iter=200, 24 | memory_size=500, 25 | batch_size=32, 26 | e_greedy_increment=None, 27 | output_graph=False, 28 | dueling=True, 29 | sess=None, 30 | ): 31 | self.n_actions = n_actions 32 | self.n_features = n_features 33 | self.lr = learning_rate 34 | self.gamma = reward_decay 35 | self.epsilon_max = e_greedy 36 | self.replace_target_iter = replace_target_iter 37 | self.memory_size = memory_size 38 | self.batch_size = batch_size 39 | self.epsilon_increment = e_greedy_increment 40 | self.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max 41 | 42 | self.dueling = dueling # whether to use dueling DQN 43 | 44 | self.learn_step_counter = 0 45 | self.memory = np.zeros((self.memory_size, n_features * 2 + 2)) 46 | self._build_net() 47 | e_params = tf.get_collection('eval_net_params') 48 | t_params = tf.get_collection('target_net_params') 49 | self.replace_target_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)] 50 | 51 | if sess is None: 52 | self.sess = tf.Session() 53 | self.sess.run(tf.global_variables_initializer()) 54 | else: 55 | self.sess = sess 56 | if output_graph: 57 | tf.summary.FileWriter("logs/", self.sess.graph) 58 | self.loss_his = [] 59 | 60 | 61 | def _build_net(self): 62 | def build_layers(s, c_names, n_l1, n_l2,w_initializer, b_initializer): 63 | with tf.variable_scope('l1'): 64 | w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names) 65 | b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names) 66 | l1 = tf.nn.relu(tf.matmul(s, w1) + b1) 67 | with tf.variable_scope('h1'): 68 | wh1 = tf.get_variable('wh1', [n_l1, 100], initializer=w_initializer, collections=c_names) 69 | bh1 = tf.get_variable('bh1', [1, 100], initializer=b_initializer, collections=c_names) 70 | lh1 = tf.nn.relu(tf.matmul(l1, wh1) + bh1) 71 | with tf.variable_scope('h2'): 72 | wh2 = tf.get_variable('wh2', [100, n_l2], initializer=w_initializer, collections=c_names) 73 | bh2 = tf.get_variable('bh2', [1, n_l2], initializer=b_initializer, collections=c_names) 74 | lh2 = tf.nn.relu(tf.matmul(lh1, wh2) + bh2) 75 | 76 | if self.dueling: 77 | # Dueling DQN 78 | with tf.variable_scope('Value'): 79 | w2 = tf.get_variable('w2', [n_l2, 1], initializer=w_initializer, collections=c_names) 80 | b2 = tf.get_variable('b2', [1, 1], initializer=b_initializer, collections=c_names) 81 | self.V = tf.matmul(lh2, w2) + b2 82 | 83 | with tf.variable_scope('Advantage'): 84 | w2 = tf.get_variable('w2', [n_l2, self.n_actions], initializer=w_initializer, collections=c_names) 85 | b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names) 86 | self.A = tf.matmul(lh2, w2) + b2 87 | 88 | with tf.variable_scope('Q'): 89 | out = self.V + (self.A - tf.reduce_mean(self.A, axis=1, keep_dims=True)) # Q = V(s) + A(s,a) 90 | else: 91 | with tf.variable_scope('Q'): 92 | w2 = tf.get_variable('w2', [n_l2, self.n_actions], initializer=w_initializer, collections=c_names) 93 | b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names) 94 | out = tf.matmul(lh2, w2) + b2 95 | 96 | return out 97 | 98 | # ------------------ build evaluate_net ------------------ 99 | self.s = tf.placeholder(tf.float32, [None, self.n_features], name='s') # input 100 | self.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name='Q_target') # for calculating loss 101 | with tf.variable_scope('eval_net'): 102 | c_names, n_l1, n_l2, w_initializer, b_initializer = \ 103 | ['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES], 20, 20, \ 104 | tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1) # config of layers 105 | 106 | self.q_eval = build_layers(self.s, c_names, n_l1, n_l2, w_initializer, b_initializer) 107 | 108 | with tf.variable_scope('loss'): 109 | self.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval)) 110 | with tf.variable_scope('train'): 111 | self._train_op = tf.train.AdamOptimizer(self.lr).minimize(self.loss) 112 | 113 | # ------------------ build target_net ------------------ 114 | self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_') # input 115 | with tf.variable_scope('target_net'): 116 | c_names = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES] 117 | 118 | self.q_next = build_layers(self.s_, c_names, n_l1,n_l2, w_initializer, b_initializer) 119 | 120 | def store_transition(self, s, a, r, s_): 121 | if not hasattr(self, 'memory_counter'): 122 | self.memory_counter = 0 123 | transition = np.hstack((s, [a, r], s_)) 124 | index = self.memory_counter % self.memory_size 125 | self.memory[index, :] = transition 126 | self.memory_counter += 1 127 | 128 | def choose_action(self, observation): 129 | observation = observation[np.newaxis, :] 130 | if np.random.uniform() < self.epsilon: # choosing action 131 | actions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation}) 132 | action = np.argmax(actions_value) 133 | else: 134 | action = np.random.randint(0, self.n_actions) 135 | return action 136 | 137 | def learn(self): 138 | if self.learn_step_counter % self.replace_target_iter == 0: 139 | self.sess.run(self.replace_target_op) 140 | print('\ntarget_params_replaced\n') 141 | 142 | sample_index = np.random.choice(self.memory_size, size=self.batch_size) 143 | batch_memory = self.memory[sample_index, :] 144 | 145 | q_next = self.sess.run(self.q_next, feed_dict={self.s_: batch_memory[:, -self.n_features:]}) # next observation 146 | q_eval = self.sess.run(self.q_eval, {self.s: batch_memory[:, :self.n_features]}) 147 | 148 | q_target = q_eval.copy() 149 | 150 | batch_index = np.arange(self.batch_size, dtype=np.int32) 151 | eval_act_index = batch_memory[:, self.n_features].astype(int) 152 | reward = batch_memory[:, self.n_features + 1] 153 | 154 | q_target[batch_index, eval_act_index] = reward + self.gamma * np.max(q_next, axis=1) 155 | 156 | self._, self.loss_ = self.sess.run([self._train_op, self.loss], 157 | feed_dict={self.s: batch_memory[:, :self.n_features], 158 | self.q_target: q_target}) 159 | 160 | self.loss_his.append(self.loss_) 161 | 162 | self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max 163 | self.learn_step_counter += 1 164 | 165 | def plot_lost(self, name='DuelingDQN', userNum='5'): 166 | data = pd.DataFrame(self.loss_his, 167 | index=np.arange(len(self.loss_his)), 168 | columns=['loss']) 169 | data.to_csv('./results/' + 170 | '_' + name + str(userNum) + '.csv') 171 | 172 | plt.plot(np.arange(len(self.loss_his)), self.loss_his) 173 | plt.ylabel('loss') 174 | plt.xlabel('training steps') 175 | plt.savefig('./results/' + 176 | name + str(userNum) + 'loss.svg', format='svg', dpi=400) 177 | plt.savefig('./results/' + 178 | name + str(userNum) + 'loss.png', format='png', dpi=400) 179 | plt.show() 180 | 181 | 182 | 183 | 184 | 185 | -------------------------------------------------------------------------------- /RL_PRDQN.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import pandas as pd 3 | 4 | """ 5 | DQN with prioritized experience replay 6 | """ 7 | 8 | import numpy as np 9 | import matplotlib.pyplot as plt 10 | import pandas as pd 11 | import tensorflow.compat.v1 as tf 12 | tf.disable_v2_behavior() 13 | 14 | np.random.seed(15) 15 | 16 | 17 | # tf.set_random_seed(1) 18 | 19 | 20 | class SumTree(object): 21 | """ 22 | Describe the data with the priority in the tree 23 | """ 24 | data_pointer = 0 25 | 26 | def __init__(self, capacity): 27 | self.capacity = capacity # for all priority values 28 | self.tree = np.zeros(2 * capacity - 1) 29 | 30 | self.data = np.zeros(capacity, dtype=object) # for all transitions 31 | 32 | 33 | def add(self, p, data): 34 | tree_idx = self.data_pointer + self.capacity - 1 35 | self.data[self.data_pointer] = data # update data_frame 36 | self.update(tree_idx, p) # update tree_frame 37 | 38 | self.data_pointer += 1 39 | if self.data_pointer >= self.capacity: # replace when exceed the capacity 40 | self.data_pointer = 0 41 | 42 | def update(self, tree_idx, p): 43 | change = p - self.tree[tree_idx] 44 | self.tree[tree_idx] = p 45 | # then propagate the change through tree 46 | while tree_idx != 0: # this method is faster than the recursive loop in the reference code 47 | tree_idx = (tree_idx - 1) // 2 48 | self.tree[tree_idx] += change 49 | 50 | def get_leaf(self, v): 51 | parent_idx = 0 52 | while True: # the while loop is faster than the method in the reference code 53 | cl_idx = 2 * parent_idx + 1 # this leaf's left and right kids 54 | cr_idx = cl_idx + 1 55 | if cl_idx >= len(self.tree): # reach bottom, end search 56 | leaf_idx = parent_idx 57 | break 58 | else: # downward search, always search for a higher priority node 59 | if v <= self.tree[cl_idx]: 60 | parent_idx = cl_idx 61 | else: 62 | v -= self.tree[cl_idx] 63 | parent_idx = cr_idx 64 | 65 | data_idx = leaf_idx - self.capacity + 1 66 | return leaf_idx, self.tree[leaf_idx], self.data[data_idx] 67 | 68 | @property 69 | def total_p(self): 70 | return self.tree[0] # the root 71 | 72 | 73 | class Memory(object): # stored as ( s, a, r, s_ ) in SumTree 74 | """ 75 | https://github.com/jaara/AI-blog/blob/master/Seaquest-DDQN-PER.py 76 | """ 77 | epsilon = 0.01 # small amount to avoid zero priority 78 | alpha = 0.6 # [0~1] convert the importance of TD error to priority 79 | beta = 0.4 # importance-sampling, from initial value increasing to 1 80 | beta_increment_per_sampling = 0.001 81 | abs_err_upper = 1. # clipped abs error 82 | 83 | def __init__(self, capacity): 84 | self.tree = SumTree(capacity) 85 | 86 | def store(self, transition): 87 | max_p = np.max(self.tree.tree[-self.tree.capacity:]) 88 | if max_p == 0: 89 | max_p = self.abs_err_upper 90 | self.tree.add(max_p, transition) # set the max p for new p 91 | 92 | def sample(self, n): 93 | b_idx, b_memory, ISWeights = np.empty((n,), dtype=np.int32), np.empty((n, self.tree.data[0].size)), np.empty( 94 | (n, 1)) 95 | pri_seg = self.tree.total_p / n # priority segment 96 | self.beta = np.min([1., self.beta + self.beta_increment_per_sampling]) # max = 1 97 | 98 | min_prob = np.min(self.tree.tree[-self.tree.capacity:]) / self.tree.total_p # for later calculate ISweight 99 | for i in range(n): 100 | a, b = pri_seg * i, pri_seg * (i + 1) 101 | v = np.random.uniform(a, b) 102 | idx, p, data = self.tree.get_leaf(v) 103 | prob = p / self.tree.total_p 104 | if min_prob == 0: 105 | ISWeights[i, 0] = np.power(prob / 0.0001, -self.beta) 106 | else: 107 | ISWeights[i, 0] = np.power(prob / min_prob, -self.beta) 108 | b_idx[i], b_memory[i, :] = idx, data 109 | return b_idx, b_memory, ISWeights 110 | 111 | def batch_update(self, tree_idx, abs_errors): 112 | abs_errors += self.epsilon # convert to abs and avoid 0 113 | clipped_errors = np.minimum(abs_errors, self.abs_err_upper) 114 | ps = np.power(clipped_errors, self.alpha) 115 | for ti, p in zip(tree_idx, ps): 116 | self.tree.update(ti, p) 117 | 118 | 119 | class PRDQN(object): 120 | def __init__( 121 | self, 122 | n_actions, 123 | n_features, 124 | learning_rate=0.005, 125 | reward_decay=0.9, 126 | e_greedy=0.9, 127 | replace_target_iter=500, 128 | memory_size=10000, 129 | batch_size=32, 130 | e_greedy_increment=0.05, 131 | output_graph=False, 132 | prioritized=True, 133 | sess=None, 134 | ): 135 | self.n_actions = n_actions 136 | self.n_features = n_features 137 | self.lr = learning_rate 138 | self.gamma = reward_decay 139 | self.epsilon_max = e_greedy 140 | self.replace_target_iter = replace_target_iter 141 | self.memory_size = memory_size 142 | self.batch_size = batch_size 143 | self.epsilon_increment = e_greedy_increment 144 | self.epsilon = 0.1 if e_greedy_increment is not None else self.epsilon_max 145 | 146 | self.prioritized = True # decide to use double q or not 147 | 148 | self.learn_step_counter = 0 149 | 150 | self._build_net() 151 | e_params = tf.get_collection('eval_net_params') 152 | t_params = tf.get_collection('target_net_params') 153 | self.replace_target_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)] 154 | 155 | if self.prioritized: 156 | self.memory = Memory(capacity=memory_size) 157 | else: 158 | self.memory = np.zeros((self.memory_size, n_features * 2 + 2)) 159 | 160 | if sess is None: 161 | self.sess = tf.Session() 162 | self.sess.run(tf.global_variables_initializer()) 163 | else: 164 | self.sess = sess 165 | 166 | if output_graph: 167 | tf.summary.FileWriter("logs/", self.sess.graph) 168 | 169 | self.loss_his = [] 170 | 171 | def _build_net(self): 172 | def build_layers(s, c_names, n_l1, n_l2, w_initializer, b_initializer, trainable): 173 | with tf.variable_scope('l1'): 174 | w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names, 175 | trainable=trainable) 176 | b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names, 177 | trainable=trainable) 178 | l1 = tf.nn.relu(tf.matmul(s, w1) + b1) 179 | with tf.variable_scope('h1'): 180 | wh1 = tf.get_variable('wh1', [n_l1, 100], initializer=w_initializer, collections=c_names) 181 | bh1 = tf.get_variable('bh1', [1, 100], initializer=b_initializer, collections=c_names) 182 | lh1 = tf.nn.relu(tf.matmul(l1, wh1) + bh1) 183 | 184 | with tf.variable_scope('h2'): 185 | wh2 = tf.get_variable('wh2', [100, n_l2], initializer=w_initializer, collections=c_names) 186 | bh2 = tf.get_variable('bh2', [1, n_l2], initializer=b_initializer, collections=c_names) 187 | lh2 = tf.nn.relu(tf.matmul(lh1, wh2) + bh2) 188 | 189 | with tf.variable_scope('l2'): 190 | w2 = tf.get_variable('w2', [n_l2, self.n_actions], initializer=w_initializer, collections=c_names) 191 | b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names) 192 | out = tf.matmul(lh2, w2) + b2 193 | return out 194 | 195 | # ------------------ build evaluate_net ------------------ 196 | self.s = tf.placeholder(tf.float32, [None, self.n_features], name='s') # input 197 | self.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name='Q_target') # for calculating loss 198 | if self.prioritized: 199 | self.ISWeights = tf.placeholder(tf.float32, [None, 1], name='IS_weights') 200 | with tf.variable_scope('eval_net'): 201 | c_names, n_l1, n_l2, w_initializer, b_initializer = \ 202 | ['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES], 20, 20,\ 203 | tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1) # config of layers 204 | 205 | self.q_eval = build_layers(self.s, c_names, n_l1,n_l2, w_initializer, b_initializer, True) 206 | 207 | with tf.variable_scope('loss'): 208 | if self.prioritized: 209 | self.abs_errors = tf.reduce_sum(tf.abs(self.q_target - self.q_eval), axis=1) # for updating Sumtree 210 | self.loss = tf.reduce_mean(self.ISWeights * tf.squared_difference(self.q_target, self.q_eval)) 211 | else: 212 | self.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval)) 213 | with tf.variable_scope('train'): 214 | self._train_op = tf.train.AdamOptimizer(self.lr).minimize(self.loss) 215 | 216 | # ------------------ build target_net ------------------ 217 | self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_') # input 218 | with tf.variable_scope('target_net'): 219 | c_names = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES] 220 | self.q_next = build_layers(self.s_, c_names, n_l1,n_l2, w_initializer, b_initializer, False) 221 | 222 | def store_transition(self, s, a, r, s_): 223 | if self.prioritized: # prioritized replay 224 | transition = np.hstack((s, [a, r], s_)) 225 | self.memory.store(transition) # have high priority for newly arrived transition 226 | else: # random replay 227 | if not hasattr(self, 'memory_counter'): 228 | self.memory_counter = 0 229 | transition = np.hstack((s, [a, r], s_)) 230 | index = self.memory_counter % self.memory_size 231 | self.memory[index, :] = transition 232 | self.memory_counter += 1 233 | 234 | def choose_action(self, observation): 235 | observation = observation[np.newaxis, :] 236 | if np.random.uniform() < self.epsilon: 237 | 238 | actions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation}) 239 | action = np.argmax(actions_value) 240 | else: 241 | action = np.random.randint(0, self.n_actions) 242 | return action 243 | 244 | def learn(self): 245 | if self.learn_step_counter % self.replace_target_iter == 0: 246 | self.sess.run(self.replace_target_op) 247 | print('\ntarget_params_replaced\n') 248 | 249 | if self.prioritized: 250 | tree_idx, batch_memory, ISWeights = self.memory.sample(self.batch_size) 251 | else: 252 | sample_index = np.random.choice(self.memory_size, size=self.batch_size) 253 | batch_memory = self.memory[sample_index, :] 254 | 255 | q_next, q_eval = self.sess.run( 256 | [self.q_next, self.q_eval], 257 | feed_dict={self.s_: batch_memory[:, -self.n_features:], 258 | self.s: batch_memory[:, :self.n_features]}) 259 | 260 | q_target = q_eval.copy() 261 | batch_index = np.arange(self.batch_size, dtype=np.int32) 262 | eval_act_index = batch_memory[:, self.n_features].astype(int) 263 | reward = batch_memory[:, self.n_features + 1] 264 | 265 | q_target[batch_index, eval_act_index] = reward + self.gamma * np.max(q_next, axis=1) 266 | 267 | if self.prioritized: 268 | _, abs_errors, self.loss_ = self.sess.run([self._train_op, self.abs_errors, self.loss], 269 | feed_dict={self.s: batch_memory[:, :self.n_features], 270 | self.q_target: q_target, 271 | self.ISWeights: ISWeights}) 272 | self.memory.batch_update(tree_idx, abs_errors) # update priority 273 | else: 274 | _, self.loss_ = self.sess.run([self._train_op, self.loss], 275 | feed_dict={self.s: batch_memory[:, :self.n_features], 276 | self.q_target: q_target}) 277 | 278 | self.loss_his.append(self.loss_) 279 | 280 | self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max 281 | self.learn_step_counter += 1 282 | 283 | def plot_lost(self, name='PRDQN', userNum='5'): 284 | data = pd.DataFrame(self.loss_his, 285 | index=np.arange(len(self.loss_his)), 286 | columns=['loss']) 287 | data.to_csv('./results/' + 288 | '_' + name + str(userNum) + '.csv') 289 | 290 | plt.plot(np.arange(len(self.loss_his)), self.loss_his) 291 | plt.ylabel('loss') 292 | plt.xlabel('training steps') 293 | plt.savefig('./results/' + 294 | name + str(userNum) + 'loss.svg', format='svg', dpi=400) 295 | plt.savefig('./results/' + 296 | name + str(userNum) + 'loss.png', format='png', dpi=400) 297 | plt.show() 298 | 299 | 300 | 301 | -------------------------------------------------------------------------------- /dataset/edgeResources-melbCBD.csv: -------------------------------------------------------------------------------- 1 | ID,Latitude,Longitude,Block,Level,Parent,State,Details 2 | 0,-37.8136,144.9631,0,0,-1,VIC,DataCenter 3 | 1,-37.813946,144.954633,1,1,0,VIC,Block1 Proxy 4 | 2,-37.816762,144.955915,2,1,0,VIC,Block2 Proxy 5 | 3,-37.818903,144.95694,3,1,0,VIC,Block3 Proxy 6 | 4,-37.812591,144.959656,4,1,0,VIC,Block4 Proxy 7 | 5,-37.815423,144.960901,5,1,0,VIC,Block5 Proxy 8 | 6,-37.817443,144.961784,6,1,0,VIC,Block6 Proxy 9 | 7,-37.811136,144.964557,7,1,0,VIC,Block7 Proxy 10 | 8,-37.813741,144.965706,8,1,0,VIC,Block8 Proxy 11 | 9,-37.815953,144.96675,9,1,0,VIC,Block9 Proxy 12 | 10,-37.809728,144.969459,10,1,0,VIC,Block10 Proxy 13 | 11,-37.812124,144.970587,11,1,0,VIC,Block11 Proxy 14 | 12,-37.814584,144.971705,12,1,0,VIC,Block12 Proxy 15 | 13,-37.81517,144.97476,12,2,12,VIC,Spring and Flinders ucell Optus North West corner Spring and Flinders St MELBOURNE 16 | 14,-37.81524,144.95256,1,2,1,VIC,Optus Minicell - Lon_Spencer Corner Spencer and Lonsdale St MELBOURNE 17 | 15,-37.81239,144.9712,11,2,11,VIC,136 Exhibition St MELBOURNE 18 | 16,-37.81679,144.96918,9,2,9,VIC,Federation Square North -V 164A Flinders Street MELBOURNE 19 | 17,-37.81808,144.95692,3,2,3,VIC,KING ST (3144 REPLACEMENT) -V 530 Collins Street MELBOURNE 20 | 18,-37.811269,144.957909,4,2,4,VIC,Empire Apartments 402-408 La Trobe Street MELBOURNE 21 | 19,-37.809081,144.96893,10,2,10,VIC,CMTS Site 287-293 Exhibition St MELBOURNE 22 | 20,-37.81285,144.955026,1,2,1,VIC,CGU Bldg 485 La Trobe Street MELBOURNE 23 | 21,-37.816356,144.962313,6,2,6,VIC,360 Collins St Collins Wales Building MELBOURNE 24 | 22,-37.818071,144.957211,3,2,3,VIC,Stock Exchange Bldg 530 Collins Street MELBOURNE 25 | 23,-37.818202,144.954367,2,2,2,VIC,625 Lt Collins St MELBOURNE 26 | 24,-37.814225,144.971971,12,2,12,VIC,ANZ Bank Tower 55 Collins Street MELBOURNE 27 | 25,-37.815975,144.955905,2,2,2,VIC,Bourke Place 600 Bourke Street MELBOURNE 28 | 26,-37.815493,144.956714,2,2,2,VIC,Marland House 570 Bourke Street MELBOURNE 29 | 27,-37.81852,144.957141,3,2,3,VIC,Rialto Towers 525 Collins Street MELBOURNE 30 | 28,-37.817303,144.962344,6,2,6,VIC,367 Collins Street Optus Centre MELBOURNE 31 | 29,-37.818162,144.959934,6,2,6,VIC,Optus Site National Mutual Building 447 Collins St MELBOURNE 32 | 30,-37.813381,144.959612,4,2,4,VIC,CMTS Site 235 Queen St MELBOURNE 33 | 31,-37.812084,144.967997,11,2,11,VIC,CMTS Site 180 Russell St MELBOURNE 34 | 32,-37.818494,144.957705,3,2,3,VIC,Optus Site Rialto 525 Collins Street MELBOURNE 35 | 33,-37.812122,144.97071,11,2,11,VIC,Optus Site CBD123 Cnr Bourkr & Exhibition Streets MELBOURNE 36 | 34,-37.811625,144.964919,7,2,7,VIC,Optus Site Lonsdale/Swanston 253 Lonsdale St MELBOURNE 37 | 35,-37.814263,144.972105,12,2,12,VIC,Optus Site 101 Collins St (Pac Dun) 101 Collins St MELBOURNE 38 | 36,-37.815549,144.966686,9,2,9,VIC,Optus Site Collins & Swanston Cnr Collins & Swanston Sts MELBOURNE 39 | 37,-37.818166,144.964831,6,2,6,VIC,Optus Site Flinders/Elizabeth 1 Elizabeth St MELBOURNE 40 | 38,-37.817384,144.959217,6,2,6,VIC,Optus Site Melbourne City West 454 Collins St MELBOURNE 41 | 39,-37.814896,144.971668,12,2,12,VIC,Optus Site Collins PLace/ANZ Tower 55 Collins St MELBOURNE 42 | 40,-37.811126,144.962116,5,2,5,VIC,Optus Site Melbourne Central Tower 360 Elizabeth St MELBOURNE 43 | 41,-37.818656,144.956626,3,2,3,VIC,Optus Site Minicell - King & Collins 539-557 Collins St MELBOURNE 44 | 42,-37.817261,144.962515,6,2,6,VIC,Optus Site Bridge Information Systems 360 Collins St MELBOURNE 45 | 43,-37.812845,144.968248,11,2,11,VIC,Optus Site Bourke/Russell 175 Bourke St MELBOURNE 46 | 44,-37.811978,144.962388,5,2,5,VIC,Optus Site Lonsdale/Elizabeth Elizabeth & Lonsdale MELBOURNE 47 | 45,-37.812723,144.960164,4,2,4,VIC,Optus Site Queen St 250 Queen St MELBOURNE 48 | 46,-37.813463,144.973217,12,2,12,VIC,Optus Site Minicell - Spring & Collins 14-16 Collins St MELBOURNE 49 | 47,-37.810996,144.967095,7,2,7,VIC,Optus Site Minicell - Lonsdale & Russell 177-183 Lonsdale St MELBOURNE 50 | 48,-37.82091,144.955155,3,2,3,VIC,Optus Site Minicell - Flinders and Spencer 2 Spencer St MELBOURNE 51 | 49,-37.820123,144.957552,3,2,3,VIC,Optus Site Minicell - King & Flinders King & Flinders St MELBOURNE 52 | 50,-37.812899,144.959886,4,2,4,VIC,Optus Site Lonsdale/Queen 250 Queen St MELBOURNE 53 | 51,-37.811678,144.962783,7,2,7,VIC,Optus Site CBD North 360 Elizabeth St MELBOURNE 54 | 52,-37.819576,144.95985,3,2,3,VIC,Optus Site Flinders/William 1 William St MELBOURNE 55 | 53,-37.817069,144.961828,6,2,6,VIC,Optus Site Minicell - Collins & Queen 379 Collins St MELBOURNE 56 | 54,-37.818218,144.960705,6,2,6,VIC,Optus Sire Minicell - Market & Flinders 50 Market St MELBOURNE 57 | 55,-37.809885,144.963286,7,2,7,VIC,Optus Site Museum Station Cnr Latrobe and Elizabeth Sts MELBOURNE 58 | 56,-37.812799,144.954686,1,2,1,VIC,Optus Site CGU Centre 485 Latrobe Street MELBOURNE 59 | 57,-37.811928,144.956414,1,2,1,VIC,Optus Site Flagstaff Station 433 Latrobe Street MELBOURNE 60 | 58,-37.816011,144.972058,12,2,12,VIC,Optus Site Minicell-Flinders & Exhibition 82 Flinders Street MELBOURNE 61 | 59,-37.814431,144.954653,1,2,1,VIC,Optus Site Minicell - Lonsdale & King 600-610 Lonsdale St MELBOURNE 62 | 60,-37.810382,144.971223,10,2,10,VIC,Optus Site TAC Building 222 Exhibition Street MELBOURNE 63 | 61,-37.816467,144.958334,2,2,2,VIC,Optus Site BHP House 140 William Street MELBOURNE 64 | 62,-37.815777,144.970508,12,2,12,VIC,Optus Site BHP Petroleum Plaza 120 Collins Street MELBOURNE 65 | 63,-37.812578,144.965314,8,2,8,VIC,Optus Site Minicell - Little Bourke & Swa Ltl Bourke & Swanston St MELBOURNE 66 | 64,-37.81607,144.957254,2,2,2,VIC,Optus Site Minicell - Bourke & William 121 William St MELBOURNE 67 | 65,-37.814398,144.96632,8,2,8,VIC,Optus Site Minicell - Town Hall 134-142 Swanston Street MELBOURNE 68 | 66,-37.813963,144.957301,1,2,1,VIC,Optus Site Minicell - Lonsdale & William Lonsdale & William Streets MELBOURNE 69 | 67,-37.817779,144.955969,2,2,2,VIC,99 King Street MELBOURNE 70 | 68,-37.818018,144.964596,6,2,6,VIC,1 Elizabeth Street cnr Flinders St MELBOURNE 71 | 69,-37.812857,144.96843,11,2,11,VIC,Optus/Telstra Site cnr Russell & Bourke Sts MELBOURNE 72 | 70,-37.811627,144.965056,7,2,7,VIC,CMTS Site cnr Swanston & Lonsdale Sts MELBOURNE 73 | 71,-37.816122,144.964421,9,2,9,VIC,Optus/Telstra Site cnr Collins & Elizabeth Sts MELBOURNE 74 | 72,-37.813447,144.966028,8,2,8,VIC,Optus Site cnr Bourke & Swanston Sts MELBOURNE 75 | 73,-37.815488,144.966824,9,2,9,VIC,cnr Collins & Swanston Streets Westin Hotel MELBOURNE 76 | 74,-37.814765,144.969286,12,2,12,VIC,Telstra/Optus Site cnr Russell & Collins Sts MELBOURNE 77 | 75,-37.812088,144.970836,11,2,11,VIC,Optus/Telstra Site cnr Bourke & Exhibition Sts MELBOURNE 78 | 76,-37.816206,144.966634,9,2,9,VIC,Optus/Vodafone Site 55 Swanston Walk MELBOURNE 79 | 77,-37.819471,144.960069,6,2,6,VIC,Optus Site 1 William St MELBOURNE 80 | 78,-37.810219,144.9618,5,2,5,VIC,Optus/Telstra Site cnr Elizabeth & La Trobe Sts MELBOURNE 81 | 79,-37.809538,144.964091,7,2,7,VIC,Optus/Telstra Site cnr Swanston & La Trobe Sts MELBOURNE 82 | 80,-37.812653,144.956622,1,2,1,VIC,Nubrick House 271 William Street MELBOURNE 83 | 81,-37.811251,144.964157,7,2,7,VIC,Legacy House 283 Swanston Street MELBOURNE 84 | 82,-37.81354,144.971421,12,2,12,VIC,Level 6 / 161 Collins Street MELBOURNE 85 | 83,-37.809969,144.97078,10,2,10,VIC,Telstra/Vodafone Site 43 Lonsdale St MELBOURNE 86 | 84,-37.809264,144.971708,10,2,10,VIC,Casselden Place 2 Lonsdale St MELBOURNE 87 | 85,-37.812711,144.959971,4,2,4,VIC,Optus Site 250 Queen St MELBOURNE 88 | 86,-37.81653,144.961967,6,2,6,VIC,394 Collins St MELBOURNE 89 | 87,-37.815387,144.968815,9,2,9,VIC,161 Collins St Carpark MELBOURNE 90 | 88,-37.81751,144.959237,6,2,6,VIC,Vodafone Microcell Cnr Collins & William Streets MELBOURNE 91 | 89,-37.814257,144.96337,8,2,8,VIC,Vodafone Microcell Cnr Bourke & Elizabeth Streets MELBOURNE 92 | 90,-37.809041,144.971828,10,2,10,VIC,Level 33 Casselden Place 235 Spring Street MELBOURNE 93 | 91,-37.820744,144.955485,3,2,3,VIC,Hotham Hotel 2 Spencer St MELBOURNE 94 | 92,-37.817483,144.959445,6,2,6,VIC,Optus Site 454 Collins St MELBOURNE 95 | 93,-37.812934,144.952075,1,2,1,VIC,Optus Site 318-326 Spencer St MELBOURNE 96 | 94,-37.812343,144.954135,1,2,1,VIC,Optus Site 450 LaTrobe St MELBOURNE 97 | 95,-37.811533,144.972614,11,2,11,VIC,Optus Site 10 Bourke St MELBOURNE 98 | 96,-37.813573,144.973404,12,2,12,VIC,Optus Site 14-16 Collins St MELBOURNE 99 | 97,-37.815793,144.958295,5,2,5,VIC,Optus Site AMP Square 121 William St MELBOURNE 100 | 98,-37.818474,144.956805,3,2,3,VIC,Optus Site 539-557 Collins St MELBOURNE 101 | 99,-37.814395,144.963537,8,2,8,VIC,S/E Cnr Bourke & Elizabeth Sts MELBOURNE 102 | 100,-37.815891,144.958861,5,2,5,VIC,140 William Street MELBOURNE 103 | 101,-37.815195,144.974409,12,2,12,VIC,Optus microcell - Cnr Spring St & Flinders St MELBOURNE 104 | 102,-37.815371,144.973076,12,2,12,VIC,8 Exhibition St MELBOURNE 105 | 103,-37.818151,144.96081,6,2,6,VIC,BTS Site 38465D Treasure Funds House 50 Market Street MELBOURNE 106 | 104,-37.814905,144.954913,1,2,1,VIC,225 King Street MELBOURNE 107 | 105,-37.812379,144.956402,1,2,1,VIC,Upper Flagstaff Station MELBOURNE 108 | 106,-37.816822,144.963209,6,2,6,VIC,333 Collins Street MELBOURNE 109 | 107,-37.815309,144.969476,12,2,12,VIC,Hyatt Hotel 123 Collins St MELBOURNE 110 | 108,-37.809356,144.970729,10,2,10,VIC,50 Lonsdale St MELBOURNE 111 | 109,-37.811313,144.972958,11,2,11,VIC,Parliament Underground Station MELBOURNE 112 | 110,-37.810641,144.970534,10,2,10,VIC,222 Exhibition St MELBOURNE 113 | 111,-37.810911,144.96269,5,2,5,VIC,cnr Elizabeth & La Trobe Streets MELBOURNE 114 | 112,-37.810065,144.963281,7,2,7,VIC,Optus/Vodafone Site 6/211 La Trobe St MELBOURNE 115 | 113,-37.811398,144.972672,11,2,11,VIC,20-30 Bourke St MELBOURNE 116 | 114,-37.816891,144.961957,6,2,6,VIC,Optus/Telstra Site cnr Collins & Queen Sts MELBOURNE 117 | 115,-37.813545,144.971705,12,2,12,VIC,Optus/Telstra Site cnr Collins & Exhibition Sts MELBOURNE 118 | 116,-37.814265,144.971685,12,2,12,VIC,CMTS Site Penthouse 71 Collins St MELBOURNE 119 | 117,-37.814484,144.9635,8,2,8,VIC,Optus/Vodafone Site 341-357 Bourke St MELBOURNE 120 | 118,-37.813167,144.965468,8,2,8,VIC,Vodafone Site 195 Swanston St MELBOURNE 121 | 119,-37.81922,144.958485,3,2,3,VIC,452-470 Flinders Street MELBOURNE 122 | 120,-37.815858,144.954818,2,2,2,VIC,Citipower House 628 Bourke Street MELBOURNE 123 | 121,-37.816217,144.970928,12,2,12,VIC,Wilson Parking 114 Flinders Street MELBOURNE 124 | 122,-37.810269,144.969863,10,2,10,VIC,Optus Minicell SE Cnr Exhibition St and Lonsdale St MELBOURNE 125 | 123,-37.819101,144.954229,3,2,3,VIC,Optus Minicell NE Cnr Spencer St and Collins St MELBOURNE 126 | 124,-37.811022,144.959234,4,2,4,VIC,Optus Minicell SE Cnr Queen St and Latrobe St MELBOURNE 127 | 125,-37.81215,144.961955,4,2,4,VIC,Optus Rooftop Site Mitchell House 283 Elizabeth Street MELBOURNE 128 | 126,-37.816496,144.96509,9,2,9,VIC,Customer Site 271 Collins Street MELBOURNE 129 | 127,-37.815296,144.958369,5,2,5,VIC,Optus Rooftop Site 520 Bourke Street MELBOURNE 130 | 128,-37.81631,144.964351,9,2,9,VIC,Optus Rooftop Site 287 Collins Street MELBOURNE 131 | 129,-37.817386,144.96123,6,2,6,VIC,Telstra IBC 401 Collins St MELBOURNE 132 | 130,-37.813175,144.952919,1,2,1,VIC,Vodafone Site Building C William Angliss Institute 555 La Trobe Street MELBOURNE VIC 3000 133 | -------------------------------------------------------------------------------- /dataset/users-melbcbd-generated.csv: -------------------------------------------------------------------------------- 1 | Latitude,Longitude 2 | -37.814619463998895,144.9744434939978 3 | -37.81013955044752,144.97045445740824 4 | -37.81989155597466,144.9573050094399 5 | -37.81452358749519,144.95363194415222 6 | -37.8141,144.963 7 | -37.811659660777174,144.96590852785317 8 | -37.81378490910957,144.95921393207493 9 | -37.81208430778482,144.957673808202 10 | -37.81490830722213,144.9702766021266 11 | -37.81524915678738,144.97314159073107 12 | -37.81048976870829,144.96983246885642 13 | -37.81511905228891,144.9672319773706 14 | -37.8193757585424,144.96072703660153 15 | -37.81493990503614,144.9571064400715 16 | -37.814904232143626,144.95946785250575 17 | -37.81234195524841,144.95747264257747 18 | -37.81255101130159,144.96912121560823 19 | -37.81297799000399,144.96500386189254 20 | -37.81563593894615,144.96891885702712 21 | -37.81421732717906,144.96868620157775 22 | -37.81149843916813,144.9578509039312 23 | -37.81755323848202,144.96367939894515 24 | -37.813824367361065,144.95564249481137 25 | -37.817498900730406,144.95733373953922 26 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-37.81389793039926,144.9706752278784 802 | -37.81692676714783,144.96148804822712 803 | -37.815806222146676,144.96322714752327 804 | -37.8140829817326,144.95687052415803 805 | -37.81352340341141,144.96135299531082 806 | -37.813404401518845,144.9658854190708 807 | -37.81034596428145,144.96425358868072 808 | -37.81492973430791,144.9609000769317 809 | -37.813493506659455,144.9703382187514 810 | -37.8182,144.957 811 | -37.819945583440024,144.95477676397226 812 | -37.809860301309755,144.96877784772568 813 | -37.81304938014091,144.95779474036004 814 | -37.8155770405011,144.96344128469474 815 | -37.80837218160124,144.9691999857174 816 | -37.80958828161427,144.968572783555 817 | -37.8154,144.963 818 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | """ 2 | simulation with multiple servers and users 3 | This code is a modified version and the original code is from https://github.com/snsong/soCoM 4 | 5 | """ 6 | 7 | import sysModel 8 | from Offload_Strategy import OFFLOAD 9 | import random 10 | import simpy 11 | import xlwt 12 | import numpy as np 13 | import tensorflow.compat.v1 as tf 14 | tf.disable_v2_behavior() 15 | import pandas as pd 16 | 17 | simTime = 144000 # simulation time is 40 hours 18 | random_seed = 40 19 | rho = 2 # the processing speed for mec 20 | buffer = 500 # mec buffer 21 | job_duration = 30 22 | mecNum = 4 23 | 24 | def other_job(env, repairman): 25 | while True: 26 | done_in = job_duration 27 | while done_in: 28 | with repairman.request(priority=2) as req: 29 | yield req 30 | try: 31 | start = env.now 32 | yield env.timeout(done_in) 33 | done_in = 0 34 | except simpy.Interrupt: 35 | done_in -= env.now - start 36 | 37 | 38 | """ NON RL simulation """ 39 | def Simulation_NONRL(rho,name,methodName,userlng, userlat, sLng, sLat, rList): 40 | random.seed(random_seed) 41 | mec_l = sysModel.MEC_L() 42 | ul = sysModel.UL() 43 | 44 | for i in range(ul.userNum): 45 | user = sysModel.User(i) 46 | user.usersetting(userlng[i],userlat[i]) 47 | user.usercreat() 48 | ul.userList.append(user) 49 | 50 | for j in range(mec_l.mecNum): 51 | mec = sysModel.MEC(j, ul) 52 | mec.RHO = rho * mec.RHO 53 | mec.setMEC(sLng[j], sLat[j], rho + j * 2, 50, rList[j]) 54 | mec_l.mecList.append(mec) 55 | 56 | name += str(ul.userNum) 57 | 58 | print("Environment create for non rl") 59 | env = simpy.Environment() 60 | 61 | repairman = simpy.PreemptiveResource(env, capacity=1) 62 | env.process(ul.mobile(env, mec_l)) # user mobile 63 | 64 | edgeservers = [sysModel.SIMRUN3(env, i, methodName, name, repairman) 65 | for i in mec_l.mecList] 66 | env.process(other_job(env, repairman)) 67 | env.run(until=simTime) 68 | 69 | for user in ul.userList: 70 | user.userprint() 71 | 72 | """ RL Simulation""" 73 | def Simulation_RL(rho, ql, name, userlng, userlat, sLng, 74 | sLat, rList): 75 | random.seed(random_seed) 76 | mec_l = sysModel.MEC_L() 77 | ul = sysModel.UL() 78 | uList = ul.userList 79 | 80 | for i in range(ul.userNum): 81 | user = sysModel.User(i) 82 | user.usersetting(userlng[i], userlat[i]) 83 | user.usercreat() 84 | ul.userList.append(user) 85 | 86 | for j in range(mec_l.mecNum): 87 | mec = sysModel.MEC(j, ul) 88 | mec.setMEC(sLng[j], sLat[j], rho + j * 2, 50, rList[j]) 89 | mec_l.mecList.append(mec) 90 | 91 | name += str(ul.userNum) 92 | print("Environment create for RL") 93 | env = simpy.Environment() 94 | 95 | 96 | repairman = simpy.PreemptiveResource(env, capacity=1) 97 | env.process(ul.mobile(env, mec_l)) # user mobile 98 | 99 | edgeservers = [sysModel.SIMRUN2(env, i, ql,name, repairman) 100 | for i in mec_l.mecList] 101 | env.process(other_job(env, repairman)) 102 | env.run(until=simTime) 103 | 104 | ul.drawtrace(name) # Print user track 105 | for u in ul.userList: 106 | u.userprint() 107 | 108 | 109 | # ---------- load data user and server location --------- 110 | userData = pd.read_csv('./dataset/users-melbcbd-generated.csv') 111 | userlng = userData['Longitude'].values.tolist() # the longitude of user 112 | userlat = userData['Latitude'].values.tolist() # the latitude of user 113 | 114 | df = pd.read_csv('./dataset/edgeResources-melbCBD.csv') 115 | sLng = df['Longitude'].tolist() # the longitude of mec 116 | sLat = df['Latitude'].tolist() # the latitude of mec 117 | 118 | radiusList = [random.uniform(100, 500) for i in range(mecNum)] # the coverage of mec 119 | 120 | 121 | # ---------- NON RL ---------- 122 | # online = 'online' + str(sysModel.CB)+'_' 123 | # Simulation_NONRL(rho,online,'online',userlng, userlat, sLng, 124 | # sLat, radiusList) 125 | # 126 | # offline = 'offline' + str(sysModel.CB)+'_' 127 | # Simulation_NONRL(rho,offline,'offline',userlng, userlat, sLng, 128 | # sLat, radiusList) 129 | 130 | 131 | # ---------- RL ---------- 132 | print("BEGIN RL Learning!") 133 | ql = OFFLOAD() 134 | ql.update(ql,random_seed, userlng, userlat, sLng, sLat, radiusList) 135 | ql.printLost() 136 | Simulation_RL(rho, ql, ql.name, userlng, userlat, sLng, sLat, 137 | radiusList) 138 | -------------------------------------------------------------------------------- /results/DuelingDQN10loss.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Deniece-long/SimforOffloading/2be133bbefcdee04bd2e2ec988d335de9e5a9fa8/results/DuelingDQN10loss.png -------------------------------------------------------------------------------- /sysModel.py: -------------------------------------------------------------------------------- 1 | 2 | """ 3 | system model with multiple mec servers, which is consist of job and user. 4 | 5 | Simulation based on SimPy: https://simpy.readthedocs.io/en/latest/. 6 | 7 | """ 8 | import random 9 | import numpy as np 10 | import matplotlib.pyplot as plt 11 | import copy 12 | import os 13 | import simpy 14 | import math 15 | import csv 16 | from math import * 17 | from Offload_Strategy import OFFLOAD 18 | from numpy import random as ra 19 | 20 | 21 | __all__ = ["Job", "User", "UL", "MEC_L", "MEC"] 22 | 23 | random.seed(1) 24 | 25 | # As the number of users increases,the frequency of unloading must be reduced, 26 | # otherwise it will be blocked 27 | 28 | UN = 10 # the number of user 29 | MECN = 4 # the number of user 30 | CB = 3 # Channel bandwidth allocation rate 31 | PEN = 1 32 | PLIST = 10 33 | PT_MEAN = 100.0 # the parameters for working time of mec 34 | PT_SIGMA = 30.0 35 | buffer = 500 # env中container 36 | REPAIR_TIME = 30 37 | 38 | class Job(object): 39 | def __init__(self, userID, jobID): 40 | self.userID = userID 41 | self.jobID = jobID 42 | self.jobTran = 0.0 # upload time 43 | self.jobDTran = 0.0 # download time 44 | self.jobRun = 0.0 # local processing time 45 | self.jobCPU = 0.0 # CPU and resource occurpy 46 | self.jobCEnergy = 0.0 # Transmission energy consumption 47 | self.jobLEnergy = 0.0 # Local execution energy consumption 48 | self.jobType = 'normal' # Task type 49 | self.jobState = 'LW' # Activation=act, printing=inh, local waiting=lw, local execution=lr, transmission=ts, remote waiting=rw, remote execution=rr, completion=cp, failure=fl 50 | self.jobValue = 1.0 # Task value 51 | 52 | # ---------- Dynamic changes during execution ---------- 53 | self.jobRunLeft = 0.0 # Task remote execution time remaining 54 | self.jobTransLeft = 0.0 # Task transfer time remaining 55 | self.jobChannel = 0.0 # The obtained channel bandwidth uses Mbps 56 | 57 | # ---------- Execution completed record ---------- 58 | self.jobBegin = 0.0 # The time when the task started 59 | self.jobFinish = 0.0 # The time when the task execution ends 60 | self.jobOffload = 0.0 # The time when the task started to unload 61 | self.jobRT =0.0 # Execution time 62 | self.jobTT = 0.0 # Transmission time 63 | self.jobAge = 0.0 # the time from the start of the offloading to the end of the execution 64 | 65 | 66 | class User(object): 67 | def __init__(self, userID): 68 | self.userID = userID 69 | self.jobList = [] # User task list 70 | self.jobData = 0.0 # Task transfer data volume 71 | self.jobTrans = [20] # Task transmission time-distribution- 72 | self.jobRuns = 20 # Task local execution time-distribution- 73 | self.jobCPU = 0.1 # Task CPU utilization 74 | self.jobNums = 50 # Initial number of tasks 75 | self.jobCEnergy = [20] # Transmission energy consumption 76 | self.jobLEnergy = [20] # local energy consumption 77 | self.jobDDL = 10 # Task deadline 78 | 79 | # ---------- location information ----------- 80 | self.X = 0.0 # 纬度 81 | self.Y = 0.0 # 经度 82 | self.speed = 0.0 83 | self.trace = [] # User movement trajectory 84 | self.userPriority = [0, 0, 0, 0] # User priority 85 | self.userMEC = [0, 0, 0, 0] # User and mec connectivity 用户与mec的连接 86 | 87 | # ---------- Log ----------- 88 | self.Throughout = 0.0 # Throughput 89 | self.CEnergy = 0.0 # Remote energy consumption 90 | self.LEnergy = 0.0 # Local energy consumption 91 | self.commTotal = 0.0 # When the transfer occurredeA 92 | self.userAge = 0.0 # user tasks are offloaded to the completion time 93 | 94 | def usersetting(self, uLng, uLat): 95 | self.jobNums = 10 96 | self.jobData = (UN - self.userID) * 8 # Decreasing data volume, kb 97 | self.jobRuns = (self.userID + 1) * 10 # job running time 98 | self.jobDDL = (self.userID + 1) * 12 # job deadline 99 | 100 | self.jobCPU = random.randint(1, UN) / (UN * 2) # Increasing resource usage 101 | self.jobLEnergy = [(self.userID + 1) * 1.25 * i for i in range(7, 25)] 102 | self.X = uLng # longitude 103 | self.Y = uLat # latitude 104 | self.trace = [[self.X], [self.Y]] 105 | self.speed = random.choice([0, 0.2, 0.4, 0.6, 0.8, 1]) # speed，m/s 用户移动速度 106 | 107 | 108 | def setjobenergy(self, jid, jobtran): 109 | BDu = self.jobList[jid].jobChannel 110 | BDd = BDu / 2 111 | self.jobList[jid].jobTran = self.jobData / BDu # Sampling from the transmission time distribution 112 | self.jobList[jid].jobDTran = self.jobData / BDd 113 | LET = BDu * 0.438 + 0.051 * BDd + 1.288 # 4G 114 | # WIFI = BDu*0.283 + 0.137*BDd + 0.132 115 | # self.JOB_LIST[jid].jobCEnergy = random.choice([LET,WIFI])*(jobtran/1000) 116 | self.jobList[jid].jobCEnergy = LET * (jobtran / 1000) 117 | 118 | def jobcreat(self, jobid, jobtype='normal'): 119 | jobrun = self.jobRuns # Sampling from the execution time distribution 120 | onejob = Job(self.userID, jobid) 121 | onejob.jobRun = jobrun 122 | onejob.jobValue = jobrun 123 | onejob.jobType = jobtype 124 | onejob.jobCPU = self.jobCPU 125 | onejob.jobLEnergy = random.choice(self.jobLEnergy) 126 | return onejob 127 | 128 | def usercreat(self): 129 | onejob = self.jobcreat(0) 130 | self.jobList.append(onejob) 131 | 132 | for i in range(1, self.jobNums): 133 | onejob = self.jobcreat(i) 134 | self.jobList.append(onejob) 135 | 136 | def userprint(self): 137 | print("User %d totalfinish %.2f, energy %.2f , age %.2f." % ( 138 | self.userID, self.Throughout, self.CEnergy, self.userAge)) 139 | 140 | def usersend(self, x, y, mecid): # User offloading algorithm 141 | jobid = -1 142 | distance = (self.X - x) ** 2 + (self.Y - y) ** 2 143 | 144 | if distance > 1e6: # Not in range 145 | self.userMEC[mecid] = 0 146 | return -1 147 | else: 148 | self.userMEC[mecid] = 1 149 | 150 | if (sum(self.userMEC) >= 2) & ( 151 | self.userPriority[mecid] < max(self.userPriority)): # Multiple connection choices and not optimal 152 | return -1 153 | for i in range(len(self.jobList)): 154 | job = self.jobList[i] 155 | 156 | if job.jobState == 'LW': 157 | jobid = i 158 | self.jobappend() # Ensure that there is a continuous task flow 159 | return jobid 160 | return jobid 161 | 162 | def userrun(self): # Local execution of the most expensive tasks 163 | jobid = -1 164 | for i in range(len(self.jobList)): 165 | job = self.jobList[i] 166 | if job.jobState == 'LW': 167 | jobid = i 168 | return jobid 169 | return jobid 170 | 171 | def jobrefresh(self, env, fjob): 172 | jobID = fjob.jobID 173 | self.Throughout += 1 174 | self.jobList[jobID].jobFinish = env.now 175 | 176 | def jobappend(self): 177 | jid = len(self.jobList) 178 | onejob = self.jobcreat(jid) 179 | self.jobList.append(onejob) 180 | 181 | def runlocal(self, env): 182 | while True: 183 | jobID = self.userrun() 184 | if jobID == -1: 185 | 186 | self.jobappend() 187 | continue 188 | else: 189 | 190 | self.jobList[jobID].jobState = 'LR' # Local execution 191 | self.jobList[jobID].jobBegin = env.now 192 | RUNNINGTIME = self.jobList[jobID].jobRun 193 | 194 | yield env.timeout(RUNNINGTIME) 195 | self.jobList[jobID].jobState = 'CP' # Finished 196 | self.LEnergy += self.jobList[jobID].jobLEnergy 197 | 198 | self.jobrefresh(env, self.jobList[jobID]) 199 | self.jobappend() 200 | 201 | 202 | class UL(object): # Total control of the movement of all users 203 | def __init__(self): 204 | self.userNum = UN 205 | self.userList = [] # user list 206 | self.frequncy = 3600.0 207 | 208 | def reset(self): 209 | self.userList = [] 210 | 211 | def M_strait(self, userID, mecList, Y, X): # Linear moving model 212 | userspeed = self.userList[userID].speed * self.frequncy / 100000 213 | angle_in_radians = random.uniform(0, 360) # 0~360度夹脚 214 | self.userList[userID].X += (userspeed * math.cos(angle_in_radians)) 215 | self.userList[userID].Y += (userspeed * math.sin(angle_in_radians)) 216 | self.userList[userID].trace[0].append(self.userList[userID].X) 217 | self.userList[userID].trace[1].append(self.userList[userID].Y) 218 | 219 | # 用于移动后，判断该用户在那个服务器范围内，然后选择即离用户最近的MEC作为结果返回 220 | mecList = [] 221 | belongMec = {} 222 | for mec in mecList: 223 | distance = mec.calcDistance('%.6f' % mec.mecY, '%.6f' % mec.mecX, '%.6f' % Y, '%.6f' % X) 224 | if distance < mec.radius : 225 | mecList.append(mec.mecID) 226 | belongMec[mec.mecID] = round(distance, 4) 227 | mec.SCORE += 1 228 | else: 229 | print('移动后用户没有在该服务器范围内') # 即用户的任务就会失败 230 | 231 | # 距离排序 小 -> 大 232 | disSort = sorted(belongMec.items(), key=lambda item: item[1], reverse=False) 233 | for mecId, distance in disSort: 234 | mecList[mecId].includUsers = userID 235 | self.ul.userList[userID].userBelongMec = True 236 | # 任务会有时间上的延迟，用户之前所在的服务器与现在所在服务器，进行对比然后把任务结果返回 237 | # 给当前所在服务器 238 | 239 | def mobile(self, env, mecList): # userrun is stratege functionname 240 | while True: 241 | yield env.timeout(self.frequncy) 242 | for u in self.userList: 243 | self.M_strait(u.userID, mecList, u.Y, u.X) 244 | 245 | def save_txt(self, list1, list2, save_path): 246 | if os.path.isfile(save_path): 247 | os.remove(save_path) 248 | with open(save_path, "a") as f: 249 | for i in range(len(list1)): 250 | f.write('{} {}\n'.format(list1[i], list2[i])) 251 | f.close() 252 | 253 | def drawtrace(self, name): # 没有使用 254 | fig = plt.figure(figsize=(15, 6)) 255 | plt.rcParams.update({'font.size': 6}) 256 | plt.title("User move") 257 | l1 = [] 258 | l2 = [] 259 | for uid in range(len(self.userList)): 260 | u = self.userList[uid] 261 | for i in range(len(u.trace[0])): 262 | l1.append(u.trace[0][i]) 263 | l2.append(u.trace[1][i]) 264 | lle = copy.copy(l1) 265 | llq = copy.copy(l2) 266 | plt.plot(lle, llq) 267 | plt.show() 268 | save_path = 'usermove/user_' + str(uid) + '.data' 269 | self.save_txt(u.trace[0], u.trace[1], save_path) 270 | 271 | 272 | 273 | class MEC_L(object): 274 | def __init__(self): 275 | self.mecNum = MECN 276 | self.mecList = [] 277 | 278 | 279 | class MEC(object): 280 | def __init__(self, mecid, userlist): 281 | # ---------- Basic Information ---------- 282 | self.ID = mecid 283 | self.userNum = UN 284 | self.ul = userlist 285 | self.CHANNEL = 50.0 # Mps, KBpms total bandwidth 286 | self.RHO = 2.0 # Local and remote execution basic rate ratio ρ 287 | self.TIMER = 10 # System status refresh frequency 288 | self.Delta = 5 # Offloading frequency=TIMER*Delta 289 | self.CB = CB # Channel bandwidth allocation factor 290 | 291 | self.mecX = 0.0 # mec location information 292 | self.mecY = 0.0 293 | self.radius = 0.0 294 | 295 | # ---------- Real-time change information 296 | self.jobPool = [] # Task list in progress 297 | self.transPool = [] # Transferring task list 298 | self.waitingList = [] # Remote waiting list 299 | self.priorityList = {} # Priority list 300 | self.channelUsed = 0.0 # Channel occupied bandwidth 301 | self.sysTime = 0.0 # System last time 302 | self.sysCPU = 0.0 # The current CPU usage of the system 303 | self.ACTION = 0 # The last action taken by the system 304 | self.SCORE = 0.0 # System last score 305 | 306 | # ---------- log ------------ 307 | self.offloadJob = [] # Task records that have been offloaded 308 | self.mecAge = 0.0 # Total age 309 | self.commTime = 0.0 # Total transmission time 310 | self.commEnergy = 0.0 # Total transmission energy consumption 311 | self.Run = 0.0 # Total execution time 312 | self.Throughout = 1 # Total offloading completed tasks 313 | self.Failure = 1 314 | 315 | # ---------- RL ---------- 316 | self.REWARD = 0.0 317 | 318 | # ----------- MEC ---------- 319 | self.fail_time = 0 # start failure time 320 | self.jobFaultProb = 0.0 # The probability of task failure 321 | self.runRemCount = 0 322 | 323 | 324 | def setMEC(self, x, y, rho, channel, r): 325 | userNum = [] 326 | coverNum = {} 327 | self.mecX = x # longitude 328 | self.mecY = y 329 | self.RHO = rho # Computing speed 330 | self.CHANNEL = channel # Bandwidth, network resources 331 | self.radius = r # coverage 332 | 333 | for u in self.ul.userList: 334 | distance = self.calcDistance('%.6f' % self.mecY, '%.6f' % self.mecX, 335 | '%.6f' % u.Y, '%.6f' % u.X) 336 | if distance < self.radius: 337 | userNum.append(u.userID) 338 | coverNum[u.userID] = round(distance, 4) 339 | 340 | 341 | # Calculate the distance between the edge server and the user 342 | def calcDistance(self, Lat_A, Lng_A, Lat_B, Lng_B): 343 | ra = 6378.140 # Equatorial radius (km) 344 | rb = 6356.755 # Polar radius (km) 345 | flatten = (ra - rb) / ra # Oblateness of the earth 346 | rad_lat_A = radians(float(Lat_A)) 347 | rad_lng_A = radians(float(Lng_A)) 348 | rad_lat_B = radians(float(Lat_B)) 349 | rad_lng_B = radians(float(Lng_B)) 350 | pA = atan(rb / ra * tan(rad_lat_A)) 351 | pB = atan(rb / ra * tan(rad_lat_B)) 352 | xx = acos(sin(pA) * sin(pB) + cos(pA) * cos(pB) * cos(rad_lng_A - rad_lng_B)) 353 | c1 = (sin(xx) - xx) * (sin(pA) + sin(pB)) ** 2 / cos(xx / 2) ** 2 354 | c2 = (sin(xx) + xx) * (sin(pA) - sin(pB)) ** 2 / sin(xx / 2) ** 2 355 | dr = flatten / 8 * (c1 - c2) 356 | distance = ra * (xx + dr) # km 357 | return distance 358 | 359 | # ---------- System log ---------- 360 | def writelog(self, env, fn, name, value, timeslot=5000): 361 | yield env.timeout(5000) 362 | f = open('./userNum/USER_' + str(fn) + '_' + str(name) + '_' + str(value) + '.data', 'w') 363 | oneline = 'TIMESLOT' + ',' + 'Throughout' + ',' + 'Failure' + ',' + 'Failurerate' + ',' + 'User' + \ 364 | ',' + 'mecAge' + ',' + 'Run' + ',' + 'commTotal' + ',' + 'commEnergy' + ',' + 'reward\n' 365 | f.write(oneline) 366 | f.close() 367 | while True: 368 | yield env.timeout(timeslot) 369 | age = 0.0 370 | run = 0.0 371 | throu = 0.0 372 | comm = 0.0 373 | energy = 0.0 374 | user = 0 375 | 376 | for i in range(self.userNum): 377 | user += self.ul.userList[i].userMEC[self.ID] 378 | 379 | sumreward = self.REWARD 380 | throu = self.Throughout 381 | fail = self.Failure 382 | age = self.mecAge / 1000 383 | run = self.Run 384 | comm = self.commTime / 1000 385 | energy = self.commEnergy 386 | sumreward = self.REWARD 387 | f = open('./userNum/USER_' + str(fn) + '_' + str(name) + '_' + str(value) + '.data', 'a') 388 | oneline = str(env.now / 1000) + ',' + str(throu) + ',' + str(fail) + ',' + str(fail / throu) + ',' + str( 389 | user) + ',' \ 390 | + str(age) + ',' + str(run) + ',' + str(comm) + ',' + str(energy) + ',' + str(sumreward) + '\n' 391 | f.write(oneline) 392 | f.close() 393 | 394 | def writeoffload(self, env,fn, name, value, timeslot=5000): 395 | yield env.timeout(5000) 396 | f1 = open('./userNum/JOB_' + str(fn) + '_' + str(name) + '_' + str(value) + '.data', 'w') 397 | titleline = ('No' + ',' + 'Uid' + ',' + 'Jid' + ',' + 'offloadtime' + ',' 398 | + 'commutime' + ',' + 'runtime' + ',' + 'energy' + ',' + 'AoI' 399 | + ',' + 'state\n') 400 | f1.write(titleline) 401 | f1.close() 402 | i = 0 403 | while True: 404 | yield env.timeout(timeslot) 405 | f2 = f1 = open('./userNum/JOB_' + str(fn) + '_' + str(name) + '_' + str(value) + '.data', 'a') 406 | for j in self.offloadJob: 407 | oneline = str(i) + ',' + str(j.userID) + ',' + str(j.jobID) + ',' + str(j.jobOffload / 1000) + ',' 408 | oneline += str(j.jobTran / 1000) + ',' + str(j.jobRun / 1000) + ',' + str(j.jobCEnergy) + ',' + str( 409 | j.jobAge / 1000) + ',' + str(j.jobState) + '\n' 410 | i += 1 411 | f2.write(oneline) 412 | f2.close() 413 | 414 | 415 | 416 | # ---------- RL learning ---------- 417 | def getstate(self): # seven system status 418 | state = [] 419 | state.append(self.channelUsed) 420 | state.append(self.sysCPU) 421 | state.append(len(self.jobPool)) 422 | state.append(len(self.transPool)) 423 | state.append(self.jobFaultProb) # The probability that a task may fail 424 | uwait = 0.0 425 | utran = 0.0 426 | for i in self.jobPool: 427 | uwait += self.ul.userList[i[0]].jobList[i[1]].jobRunLeft 428 | for j in self.transPool: 429 | utran += self.ul.userList[j[0]].jobList[j[1]].jobTransLeft 430 | state.append(uwait) 431 | state.append(utran) 432 | state = np.array(state) 433 | return state 434 | 435 | def reset(self): 436 | # ---------- Real-time change information ---------- 437 | self.jobPool = [] # Task list in progress 438 | self.transPool = [] # Transferring task list 439 | self.waitingList = [] # Remote waiting list 440 | self.channelUsed = 0.0 # Channel occupied bandwidth 441 | self.sysTime = 0.0 # System last time 442 | self.sysCPU = 0.0 # The current CPU usage of the system 443 | # ---------- log ---------- 444 | self.offloadJob = [] # Task records that have been offloaded 445 | self.REWARD = 0.0 446 | 447 | # ---------- Channel interference ---------- 448 | def channeldisturb(self, userID, jobID): # Bandwidth allocation 449 | cl = self.CHANNEL - self.channelUsed 450 | cl = cl / self.CB 451 | self.channelUsed += cl 452 | jt = self.ul.userList[userID].jobData / cl 453 | self.ul.userList[userID].jobList[jobID].jobChannel = cl 454 | 455 | return jt 456 | 457 | def offloadOne(self, env, userID): # offload a task 458 | jobID = self.ul.userList[userID].usersend(self.mecX, self.mecY, self.ID) 459 | if jobID == -1: 460 | return 461 | 462 | TRANSPOTTIME = self.channeldisturb(userID, jobID) # Really required transmission time 463 | 464 | self.ul.userList[userID].jobList[jobID].jobOffload = env.now # Task start transmission time 465 | self.ul.userList[userID].jobList[ 466 | jobID].jobState = 'TS' # Task status changes, start transmission TS -transmission 467 | self.ul.userList[userID].jobList[jobID].jobAge = env.now # Record moments 468 | self.ul.userList[userID].jobList[jobID].jobTT = TRANSPOTTIME # Task real transmission time record 469 | self.ul.userList[userID].jobList[jobID].jobTransLeft = TRANSPOTTIME # Remaining transmission time 470 | self.ul.userList[userID].setjobenergy(jobID, TRANSPOTTIME) # Task transmission energy consumption calculation 471 | self.commEnergy += self.ul.userList[userID].jobList[ 472 | jobID].jobCEnergy # The user spends the total task to transfer energy 473 | self.transPool.append((userID, jobID)) # Task joins the transfer pool 474 | 475 | 476 | def time_per_part(self,mecID): 477 | re1 = random.normalvariate(PT_MEAN, PT_SIGMA) 478 | return re1 479 | 480 | def run_remote(self, env, mecID, waitingLen, repairman): # Remote execution 481 | while True: 482 | worktime = self.time_per_part(mecID) 483 | while worktime: 484 | try: 485 | start = env.now 486 | yield env.timeout(worktime) 487 | if self.sysCPU > 0.8: # CPU overload 488 | yield env.timeout(self.TIMER * 2) # System status refresh frequency 489 | self.SCORE = -abs(self.SCORE) 490 | continue 491 | else: 492 | yield waitingLen.get(1) # Get a task from the waiting list 493 | job = self.waitingList.pop() 494 | userID = job['userID'] 495 | jobID = job['jobID'] 496 | 497 | self.jobPool.append((userID, jobID)) # Put tasks into the execution queue pool 498 | self.sysCPU += ( 499 | self.ul.userList[userID].jobList[jobID].jobCPU / self.RHO) # Real resource usage 500 | self.ul.userList[userID].jobList[jobID].jobState = 'RR' # RR-Remote execution 501 | self.ul.userList[userID].jobList[jobID].jobBegin = env.now 502 | RUNNINGTIME = float(self.ul.userList[userID].jobList[ 503 | jobID].jobRun) / self.RHO # Really required execution time 504 | self.ul.userList[userID].jobList[jobID].jobRT = RUNNINGTIME 505 | self.ul.userList[userID].jobList[jobID].jobRunLeft = RUNNINGTIME 506 | if self.fail_time > self.ul.userList[userID].jobList[jobID].jobAge: 507 | if self.ul.userList[userID].jobList[jobID].jobAge + RUNNINGTIME > self.fail_time: 508 | chazhi = self.fail_time - self.ul.userList[userID].jobList[jobID].jobAge 509 | self.jobFaultProb = chazhi / RUNNINGTIME 510 | else: 511 | self.jobFaultProb = 1 512 | self.runRemCount += 1 513 | done_in = 0 514 | except simpy.Interrupt: 515 | self.broken = True 516 | done_in -= env.now - start 517 | with repairman.request(priority=1) as req: 518 | yield req 519 | yield env.timeout(REPAIR_TIME) 520 | self.broken = False 521 | 522 | # online algorithm 523 | def runRemoteByOther(self, env, waitingLen): 524 | while True: 525 | yield env.timeout(self.TIMER) 526 | if self.sysCPU > 0.8: 527 | yield env.timeout(self.TIMER * 2) 528 | self.SCORE = -abs(self.SCORE) 529 | continue 530 | else: 531 | yield waitingLen.get(1) 532 | job = self.waitingList.pop(0) 533 | userID = job.userID 534 | jobID = job.jobID 535 | self.jobPool.append((userID, jobID)) 536 | self.sysCPU += self.ul.userList[userID].jobList[jobID].jobCPU 537 | 538 | self.ul.userList[userID].jobList[jobID].jobState = 'RR' 539 | self.ul.userList[userID].jobList[jobID].jobBegin = env.now 540 | RUNNINGTIME = float(self.ul.userList[userID].jobList[jobID].jobRun) / self.RHO 541 | self.ul.userList[userID].jobList[jobID].jobRT = RUNNINGTIME 542 | self.ul.userList[userID].jobList[jobID].jobRunLeft = RUNNINGTIME 543 | 544 | # ---------- Refresh system-transmission part ---------- 545 | def refresh_trans(self, env, waitingLen): 546 | while True: 547 | yield env.timeout(self.TIMER) 548 | transpool = [] 549 | for Jt in self.transPool: 550 | userID = Jt[0] 551 | jobID = Jt[1] 552 | onejob = self.ul.userList[userID].jobList[jobID] 553 | 554 | if onejob.jobTransLeft > self.TIMER: 555 | transpool.append((userID, jobID)) 556 | self.ul.userList[userID].jobList[jobID].jobTransLeft = self.ul.userList[userID].jobList[ 557 | jobID].jobTransLeft - self.TIMER 558 | else: 559 | self.ul.userList[userID].jobList[jobID].jobState = 'RW' # RW - remote waiting 560 | self.channelUsed -= self.ul.userList[userID].jobList[jobID].jobChannel 561 | 562 | self.waitingList.append({'userID': userID, 'jobID': jobID}) 563 | self.ul.userList[userID].jobappend() 564 | yield waitingLen.put(1) 565 | self.transPool = transpool 566 | 567 | # ---------- Refresh system-execution part ----------- 568 | def refresh_sys(self, env, name='', value='', flag=1): 569 | if flag == 1: 570 | f = open('./userNum/ACTION_' + str(name) + '_' + str(value) + '.data', 'w') 571 | oneline = ('sysTime' + ',' + 'Action' + ',' + 'ChannelUsed' + ',' + 'TransJob' + ',' + 'CPU' + ',' + 572 | 'JobFaultProb' + ',' + 'RunningJob' + ',' + 'ActionQos\n') 573 | f.write(oneline) 574 | f.close() 575 | while True: 576 | yield env.timeout(self.TIMER) 577 | self.sysTime = env.now 578 | jobpool = [] 579 | for Jr in self.jobPool: 580 | userID = Jr[0] 581 | jobID = Jr[1] 582 | onejob = self.ul.userList[userID].jobList[jobID] 583 | 584 | if onejob.jobRunLeft > self.TIMER: 585 | jobpool.append((userID, jobID)) 586 | self.ul.userList[userID].jobList[jobID].jobRunLeft = self.ul.userList[userID].jobList[ 587 | jobID].jobRunLeft - self.TIMER 588 | else: 589 | if flag == 1: 590 | f = open('./userNum/ACTION_' + str(name) + '_' + str(value) + '.data', 'a') 591 | oneline = str(self.sysTime) + ',' + str(self.ACTION) + ',' + str( 592 | self.channelUsed) + ',' + str(len(self.transPool)) + \ 593 | ',' + str(self.sysCPU) + ',' + str(self.jobFaultProb) + ',' \ 594 | + str(len(self.jobPool)) 595 | oneline += ',' + str(self.SCORE) + '\n' 596 | f.write(oneline) 597 | self.sysCPU -= (self.ul.userList[userID].jobCPU / self.RHO) 598 | self.ul.userList[userID].jobrefresh(env, self.ul.userList[userID].jobList[jobID]) 599 | self.offloadJob.append(self.ul.userList[userID].jobList[jobID]) 600 | # ---------- log ----------- 601 | self.ul.userList[userID].jobList[jobID].jobAge = env.now - self.ul.userList[userID].jobList[ 602 | jobID].jobAge 603 | self.mecAge += self.ul.userList[userID].jobList[jobID].jobAge 604 | self.Run += self.ul.userList[userID].jobList[jobID].jobRun 605 | self.commTime += self.ul.userList[userID].jobList[jobID].jobTT 606 | self.Throughout += 1 607 | # ---------- REWARD ---------- 608 | score = self.ul.userList[userID].jobList[jobID].jobRun / \ 609 | (self.ul.userList[userID].jobList[jobID].jobRT + 610 | self.ul.userList[userID].jobList[jobID].jobCEnergy) 611 | 612 | self.SCORE = score 613 | self.ul.userList[userID].jobList[jobID].jobState = 'CP' # CP-completion 614 | self.REWARD += self.SCORE 615 | self.priorityList[userID] = self.SCORE 616 | self.ul.userList[userID].userPriority[self.ID] = self.SCORE 617 | self.jobPool = jobpool 618 | f.close() 619 | 620 | 621 | # ---------- offloading ---------- 622 | def offloadDQ(self, env, waitingLen, ql): # deep Q learning 623 | counter = 0 624 | while True: 625 | counter += 1 626 | yield env.timeout(self.TIMER) 627 | 628 | if (self.CHANNEL - self.channelUsed <= 1) or (self.sysCPU > 0.9): 629 | self.SCORE = -abs(self.SCORE) 630 | yield env.timeout(self.TIMER * self.Delta) 631 | continue 632 | else: 633 | observation = self.getstate() 634 | self.ACTION = ql.RL.choose_action(observation) 635 | if (counter < UN * 5) or (counter % 10 == 0): 636 | pkey = random.sample([i for i in range(UN)], k=self.ACTION) 637 | else: 638 | plist = sorted(self.priorityList.items(), key=lambda i: i[1], reverse=True) 639 | pkey = [plist[i][0] for i in range(len(plist))][:self.ACTION] 640 | for i in range(len(pkey)): 641 | userID = pkey[i] 642 | self.offloadOne(env, userID) 643 | 644 | def time_to_failure(self): 645 | fault_time = ra.poisson(lam=100) 646 | return fault_time 647 | 648 | 649 | def break_machine(self, env, mecID, remote): 650 | while True: 651 | time = self.time_to_failure() 652 | yield env.timeout(time) 653 | self.fail_time = env.now 654 | if not self.broken: 655 | self.remote.interrupt() 656 | 657 | def randombin(self, action): 658 | userlist = list(bin(action).replace('0b', '')) 659 | zero = self.userNum - len(userlist) 660 | ll = [0 for i in range(zero)] 661 | for i in userlist: 662 | ll.append(int(i)) 663 | return ll 664 | 665 | def channelDisturbByOL(self, userID, jobID, jobnum, channel): 666 | disturb = np.log2(1 + 1 / (self.CB + jobnum)) 667 | cl = channel * disturb 668 | if self.channelUsed + cl > self.CHANNEL: 669 | return -1 670 | self.channelUsed += cl 671 | jt = self.ul.userList[userID].jobData / cl 672 | self.ul.userList[userID].jobList[jobID].jobChannel = cl 673 | return jt 674 | 675 | 676 | def offloadOneByOne(self, env, userID, jobnum, channel, x, y, mecID): 677 | jobID = self.ul.userList[userID].usersend(x, y, mecID) 678 | if jobID == -1: 679 | return 680 | 681 | TRANSPOTTIME = self.channelDisturbByOL(userID, jobID, jobnum, channel) 682 | if TRANSPOTTIME == -1: 683 | self.SCORE = -abs(self.SCORE) 684 | return 685 | self.ul.userList[userID].jobList[jobID].jobOffload = env.now # Task start transmission time 686 | self.ul.userList[userID].jobList[ 687 | jobID].jobState = 'TS' # Task status changes, start transmission TS -transmission 688 | self.ul.userList[userID].jobList[jobID].jobAge = env.now # Record birthday moments 689 | self.ul.userList[userID].jobList[jobID].jobTT = TRANSPOTTIME # Task real transmission time record 690 | self.ul.userList[userID].jobList[jobID].jobTransLeft = TRANSPOTTIME # Remaining transmission time 691 | self.ul.userList[userID].setjobenergy(jobID, TRANSPOTTIME) # Task transmission energy consumption calculation 692 | self.commEnergy += self.ul.userList[userID].jobList[ 693 | jobID].jobCEnergy # The user spends the total task to transfer energy 694 | self.transPool.append((userID, jobID)) # Task joins the transfer pool 695 | 696 | # ---------- online ---------- 697 | def offloadOL(self, env, waitingLen): 698 | while True: 699 | if self.CHANNEL - self.channelUsed <= 1: 700 | self.SCORE = -abs(self.SCORE) 701 | yield env.timeout(self.TIMER * self.Delta * 2) 702 | continue 703 | yield env.timeout(self.TIMER * self.Delta) 704 | 705 | self.ACTION = random.randint(1, 2 ** self.userNum - 1) 706 | 707 | userlist = self.randombin(self.ACTION) 708 | jobnum = sum(userlist) 709 | channel = self.CHANNEL - self.channelUsed 710 | 711 | for i in range(len(userlist)): 712 | if userlist[i] == 1: 713 | userID = i 714 | self.offloadOneByOne(env, userID, jobnum, channel, 715 | self.ul.userList[userID].X, 716 | self.ul.userList[userID].Y, 717 | self.ID) 718 | 719 | 720 | def offline(self): 721 | score = 0.0 722 | action = 1 723 | for i in range(2 ** self.ul.userNum): 724 | userlist = self.randombin(i) 725 | score_ = 0 726 | jobnum = sum(userlist) 727 | channel = self.CHANNEL - self.channelUsed 728 | cl = 0 729 | for u in range(len(userlist)): 730 | if userlist[u] == 1: 731 | userID = u 732 | disturb = np.log2(1 + 1 / (self.CB + jobnum)) 733 | cl = channel * disturb 734 | score_ += np.average(self.ul.userList[userID].jobRuns) / \ 735 | self.ul.userList[userID].jobData * cl 736 | if score_ > score: 737 | score = score_ 738 | action = i 739 | return action 740 | 741 | # ---------- offline ---------- 742 | def offloadOF(self, env, waitingLen): 743 | while True: 744 | if self.CHANNEL - self.channelUsed <= 1: 745 | self.SCORE = -abs(self.SCORE) 746 | yield env.timeout(self.TIMER * self.Delta * 2) 747 | continue 748 | yield env.timeout(self.TIMER * self.Delta) 749 | self.ACTION = self.offline() 750 | 751 | userlist = self.randombin(self.ACTION) 752 | jobnum = sum(userlist) 753 | channel = self.CHANNEL - self.channelUsed 754 | for i in range(len(userlist)): 755 | if userlist[i] == 1: 756 | userID = i 757 | self.offloadOneByOne(env, userID, jobnum, channel, 758 | self.ul.userList[userID].X, 759 | self.ul.userList[userID].Y, 760 | self.ID 761 | ) 762 | 763 | 764 | class SIMRUN(object): 765 | def __init__(self,env,mec,ql,repairman): 766 | self.env = env 767 | self.mec = mec 768 | self.broken = False 769 | 770 | waitingLen = simpy.Container(env, buffer, init=len(self.mec.waitingList)) 771 | self.remote = env.process(self.mec.run_remote(self.env, self.mec.ID,waitingLen, repairman)) 772 | env.process(self.mec.refresh_trans(env, waitingLen)) 773 | sys0 = env.process(self.mec.refresh_sys(env, flag=0)) 774 | self.env.process(self.mec.break_machine(env, self.mec.ID,self.remote)) 775 | self.env.process(ql.step(self.mec, env)) 776 | 777 | """ RL simulation""" 778 | class SIMRUN2(object): 779 | def __init__(self, env, mec, ql, name, repairman): 780 | self.env = env 781 | self.mec = mec 782 | self.name = name 783 | self.broken = False 784 | 785 | waitingLen = simpy.Container(env, buffer, init=len(self.mec.waitingList)) 786 | self.remote = env.process(self.mec.run_remote(env, self.mec.ID,waitingLen, repairman)) 787 | env.process(self.mec.refresh_trans(env, waitingLen)) 788 | env.process(self.mec.refresh_sys(env, self.name, 'mec'+str(self.mec.ID)+'_'+str(self.mec.RHO) 789 | +'_'+str(self.mec.CHANNEL))) 790 | 791 | env.process(self.mec.break_machine(env, self.mec.ID,self.remote)) 792 | env.process(self.mec.offloadDQ(env,waitingLen,ql)) 793 | env.process(self.mec.writelog(env,name,'mec'+str(self.mec.ID), 794 | str(self.mec.RHO)+str(self.mec.CHANNEL))) 795 | env.process(self.mec.writeoffload(env,name, 'mec0', str(self.mec.RHO) + str(self.mec.CHANNEL))) 796 | 797 | """ Non RL simulation""" 798 | class SIMRUN3(object): 799 | def __init__(self, env, mec, methodName, name, repairman): 800 | self.env = env 801 | self.mec = mec 802 | self.methodName = methodName 803 | self.name = name 804 | self.broken = False 805 | 806 | 807 | waitingLen = simpy.Container(env, buffer, init=len(self.mec.waitingList)) 808 | self.remote = env.process(self.mec.run_remote(env, self.mec.ID,waitingLen, repairman)) 809 | 810 | env.process(self.mec.refresh_trans(env, waitingLen)) 811 | 812 | 813 | env.process(self.mec.refresh_sys(env, self.name, 'mec'+str(self.mec.ID)+'_'+str(self.mec.RHO) 814 | +'_'+str(self.mec.CHANNEL))) 815 | 816 | env.process(self.mec.break_machine(env, self.mec.ID,self.remote)) 817 | if methodName == 'offline': 818 | env.process(self.mec.offloadOF(env,waitingLen)) 819 | elif methodName == 'online': 820 | env.process(self.mec.offloadOL(env,waitingLen)) 821 | 822 | env.process(self.mec.writelog(env,name,'mec'+str(self.mec.ID), 823 | str(self.mec.RHO)+str(self.mec.CHANNEL))) 824 | 825 | env.process(self.mec.writeoffload(env,name, 'rho', self.mec.RHO)) 826 | 827 | 828 | 829 | -------------------------------------------------------------------------------- /userNum/ACTION_offline3_10_mec0_2_50.data: -------------------------------------------------------------------------------- 1 | sysTime,Action,ChannelUsed,TransJob,CPU,JobFaultProb,RunningJob,ActionQos 2 | 170,7,-3.552713678800501e-15,0,0.175,0.24,1,0.0 3 | 290,7,-3.552713678800501e-15,0,0.025,0.3111111111111111,1,1.9998146311711043 4 | 410,7,-3.552713678800501e-15,0,0.075,0.225,1,1.99958811592774 5 | 530,7,-3.552713678800501e-15,0,0.075,1,1,1.9993050440235072 6 | 660,7,-3.552713678800501e-15,0,0.075,1,1,1.9993050440235072 7 | 790,7,-3.552713678800501e-15,0,0.025,1,1,1.9993050440235072 8 | 910,7,-3.552713678800501e-15,0,0.075,1,1,1.99958811592774 9 | 1040,7,-3.552713678800501e-15,0,0.025,1,1,1.9993050440235072 10 | 1150,7,-3.552713678800501e-15,0,0.075,1,1,1.99958811592774 11 | 1290,7,-3.552713678800501e-15,0,0.175,1,1,1.9993050440235072 12 | 1410,7,-3.552713678800501e-15,0,0.025,1,1,1.9998146311711043 13 | 1530,7,-3.552713678800501e-15,0,0.075,1,1,1.99958811592774 14 | -------------------------------------------------------------------------------- /userNum/JOB_offline3_10_rho_2.data: -------------------------------------------------------------------------------- 1 | No,Uid,Jid,offloadtime,commutime,runtime,energy,AoI,state 2 | 0,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 3 | 1,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 4 | 2,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 5 | 3,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 6 | 4,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 7 | 5,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 8 | 6,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 9 | 7,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 10 | 8,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 11 | 9,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 12 | 10,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 13 | 11,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 14 | 12,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 15 | 13,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 16 | 14,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 17 | 15,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 18 | 16,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 19 | 17,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 20 | 18,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 21 | 19,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 22 | 20,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 23 | 21,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 24 | 22,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 25 | 23,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 26 | 24,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 27 | 25,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 28 | 26,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 29 | 27,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 30 | 28,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 31 | 29,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 32 | 30,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 33 | 31,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 34 | 32,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 35 | 33,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 36 | 34,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 37 | 35,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 38 | 36,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 39 | 37,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 40 | 38,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 41 | 39,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 42 | 40,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 43 | 41,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 44 | 42,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 45 | 43,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 46 | 44,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 47 | 45,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 48 | 46,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 49 | 47,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 50 | 48,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 51 | 49,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 52 | 50,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 53 | 51,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 54 | 52,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 55 | 53,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 56 | 54,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 57 | 55,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 58 | 56,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 59 | 57,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 60 | 58,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 61 | 59,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 62 | 60,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 63 | 61,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 64 | 62,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 65 | 63,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 66 | 64,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 67 | 65,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 68 | 66,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 69 | 67,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 70 | 68,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 71 | 69,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 72 | 70,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 73 | 71,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 74 | 72,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 75 | 73,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 76 | 74,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 77 | 75,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 78 | 76,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 79 | 77,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 80 | 78,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 81 | 79,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 82 | 80,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 83 | 81,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 84 | 82,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 85 | 83,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 86 | 84,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 87 | 85,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 88 | 86,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 89 | 87,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 90 | 88,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 91 | 89,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 92 | 90,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 93 | 91,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 94 | 92,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 95 | 93,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 96 | 94,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 97 | 95,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 98 | 96,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 99 | 97,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 100 | 98,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 101 | 99,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 102 | 100,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 103 | 101,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 104 | 102,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 105 | 103,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 106 | 104,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 107 | 105,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 108 | 106,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 109 | 107,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 110 | 108,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 111 | 109,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 112 | 110,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 113 | 111,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 114 | 112,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 115 | 113,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 116 | 114,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 117 | 115,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 118 | 116,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 119 | 117,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 120 | 118,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 121 | 119,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 122 | 120,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 123 | 121,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 124 | 122,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 125 | 123,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 126 | 124,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 127 | 125,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 128 | 126,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 129 | 127,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 130 | 128,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 131 | 129,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 132 | 130,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 133 | 131,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 134 | 132,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 135 | 133,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 136 | 134,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 137 | 135,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 138 | 136,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 139 | 137,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 140 | 138,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 141 | 139,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 142 | 140,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 143 | 141,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 144 | 142,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 145 | 143,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 146 | 144,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 147 | 145,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 148 | 146,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 149 | 147,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 150 | 148,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 151 | 149,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 152 | 150,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 153 | 151,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 154 | 152,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 155 | 153,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 156 | 154,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 157 | 155,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 158 | 156,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 159 | 157,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 160 | 158,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 161 | 159,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 162 | 160,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 163 | 161,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 164 | 162,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 165 | 163,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 166 | 164,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 167 | 165,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 168 | 166,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 169 | 167,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 170 | 168,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 171 | 169,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 172 | 170,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 173 | 171,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 174 | 172,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 175 | 173,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 176 | 174,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 177 | 175,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 178 | 176,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 179 | 177,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 180 | 178,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 181 | 179,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 182 | 180,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 183 | 181,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 184 | 182,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 185 | 183,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 186 | 184,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 187 | 185,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 188 | 186,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 189 | 187,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 190 | 188,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 191 | 189,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 192 | 190,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 193 | 191,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 194 | 192,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 195 | 193,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 196 | 194,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 197 | 195,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 198 | 196,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 199 | 197,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 200 | 198,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 201 | 199,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 202 | 200,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 203 | 201,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 204 | 202,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 205 | 203,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 206 | 204,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 207 | 205,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 208 | 206,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 209 | 207,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 210 | 208,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 211 | 209,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 212 | 210,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 213 | 211,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 214 | 212,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 215 | 213,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 216 | 214,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 217 | 215,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 218 | 216,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 219 | 217,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 220 | 218,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 221 | 219,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 222 | 220,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 223 | 221,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 224 | 222,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 225 | 223,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 226 | 224,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 227 | 225,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 228 | 226,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 229 | 227,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 230 | 228,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 231 | 229,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 232 | 230,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 233 | 231,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 234 | 232,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 235 | 233,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 236 | 234,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 237 | 235,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 238 | 236,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 239 | 237,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 240 | 238,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 241 | 239,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 242 | 240,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 243 | 241,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 244 | 242,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 245 | 243,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 246 | 244,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 247 | 245,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 248 | 246,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 249 | 247,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 250 | 248,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 251 | 249,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 252 | 250,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 253 | 251,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 254 | 252,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 255 | 253,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 256 | 254,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 257 | 255,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 258 | 256,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 259 | 257,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 260 | 258,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 261 | 259,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 262 | 260,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 263 | 261,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 264 | 262,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 265 | 263,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 266 | 264,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 267 | 265,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 268 | 266,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 269 | 267,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 270 | 268,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 271 | 269,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 272 | 270,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 273 | 271,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 274 | 272,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 275 | 273,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 276 | 274,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 277 | 275,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 278 | 276,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 279 | 277,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 280 | 278,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 281 | 279,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 282 | 280,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 283 | 281,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 284 | 282,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 285 | 283,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 286 | 284,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 287 | 285,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 288 | 286,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 289 | 287,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 290 | 288,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 291 | 289,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 292 | 290,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 293 | 291,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 294 | 292,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 295 | 293,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 296 | 294,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 297 | 295,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 298 | 296,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 299 | 297,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 300 | 298,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 301 | 299,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 302 | 300,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 303 | 301,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 304 | 302,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 305 | 303,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 306 | 304,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 307 | 305,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 308 | 306,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 309 | 307,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 310 | 308,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 311 | 309,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 312 | 310,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 313 | 311,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 314 | 312,9,4,0.1,0.0007194489768963071,0.1,0.0046346502822424435,0.07,CP 315 | 313,8,12,0.2,0.0014388979537926143,0.09,0.009269300564484887,0.09,CP 316 | 314,7,20,0.3,0.002158346930688922,0.08,0.013903950846727336,0.11,CP 317 | 315,7,16,0.25,0.002158346930688922,0.08,0.013903950846727336,0.28,CP 318 | 316,7,12,0.2,0.002158346930688922,0.08,0.013903950846727336,0.46,CP 319 | 317,8,8,0.15,0.0014388979537926143,0.09,0.009269300564484887,0.64,CP 320 | 318,7,8,0.15,0.002158346930688922,0.08,0.013903950846727336,0.76,CP 321 | 319,8,4,0.1,0.0014388979537926143,0.09,0.009269300564484887,0.94,CP 322 | 320,7,4,0.1,0.002158346930688922,0.08,0.013903950846727336,1.05,CP 323 | 321,9,0,0.05,0.0007194489768963071,0.1,0.0046346502822424435,1.24,CP 324 | 322,8,0,0.05,0.0014388979537926143,0.09,0.009269300564484887,1.36,CP 325 | 323,7,0,0.05,0.002158346930688922,0.08,0.013903950846727336,1.48,CP 326 | -------------------------------------------------------------------------------- /userNum/USER_DuelingDQN10_mec0_250.data: -------------------------------------------------------------------------------- 1 | TIMESLOT,Throughout,Failure,Failurerate,User,mecAge,Run,commTotal,commEnergy,reward 2 | 10.0,81,1,0.012345679012345678,10,246.41,3350.0,4.181547940821229,71.96232071090597,159.26188007829057 3 | 15.0,121,1,0.008264462809917356,10,580.03,5110.0,8.857801969063393,91.05517435731389,238.59620203804448 4 | 20.0,161,1,0.006211180124223602,10,1044.63,6790.0,14.90680992419946,108.86652249226542,317.6351012170886 5 | 25.0,201,1,0.004975124378109453,10,1590.26,8480.0,21.523593231785544,124.62924606217211,396.6776670231067 6 | 30.0,241,1,0.004149377593360996,10,2282.96,10070.0,29.626699481858388,139.7574585704474,475.42452431625594 7 | 35.0,282,1,0.0035460992907801418,10,3010.72,11770.0,37.52996596780362,154.42108872035988,556.2511603142219 8 | 40.0,322,1,0.003105590062111801,10,3920.58,13300.0,47.41075723910177,168.1336811125875,634.7021630350222 9 | 45.0,362,1,0.0027624309392265192,10,4767.18,14950.0,55.81822669459528,183.81889613124508,713.45007197901 10 | 50.0,402,1,0.0024875621890547263,10,5707.85,16570.0,64.98350927552785,198.2264337021186,792.0523800734827 11 | 55.0,442,1,0.0022624434389140274,10,6694.29,18210.0,74.39058748239826,211.01254926322807,870.6615882600532 12 | 60.0,483,1,0.002070393374741201,10,7672.76,19880.0,83.58697805131023,226.64026013417958,951.2784472836111 13 | 65.0,523,1,0.0019120458891013384,10,8664.43,21490.0,93.01566579251802,241.5056626167016,1029.841742132444 14 | 70.0,563,1,0.0017761989342806395,10,9635.49,23140.0,102.06796146920064,255.32761010790077,1108.5073369171816 15 | 75.0,603,1,0.001658374792703151,10,10698.83,24720.0,112.06127468223838,268.4314487177258,1186.9595255107456 16 | 80.0,643,1,0.0015552099533437014,10,11769.93,26300.0,122.092831307078,281.2504698131831,1265.4105520434236 17 | 85.0,683,1,0.0014641288433382138,10,12734.62,27950.0,131.0176030544527,295.8837663958216,1344.0740002665839 18 | 90.0,724,1,0.0013812154696132596,10,13903.37,29570.0,141.71994835278926,309.7626456135136,1424.4167859562278 19 | 95.0,764,1,0.0013089005235602095,10,14956.59,31230.0,151.24828644968568,323.5244770792912,1502.9867137903627 20 | 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140.0,1126,1,0.0008880994671403197,10,24769.66,45680.0,240.38738074297092,444.65072879592117,2213.3006997569805 29 | -------------------------------------------------------------------------------- /userNum/USER_DuelingDQN10_mec1_450.data: -------------------------------------------------------------------------------- 1 | TIMESLOT,Throughout,Failure,Failurerate,User,mecAge,Run,commTotal,commEnergy,reward 2 | 10.0,70,1,0.014285714285714285,10,179.49,3180.0,2.504252796332147,53.90927272605981,273.77004039909946 3 | 15.0,105,1,0.009523809523809525,10,463.07,4870.0,5.616089449702614,66.49332871504352,411.4962855292175 4 | 20.0,140,1,0.007142857142857143,10,823.48,6490.0,9.481117548637892,78.60092261148708,548.5756881786729 5 | 25.0,174,1,0.005747126436781609,10,1261.38,8010.0,13.80684990783927,89.07447992484262,681.2189623161382 6 | 30.0,209,1,0.004784688995215311,10,1789.58,9510.0,18.929023919604937,99.14815786668267,816.958579357061 7 | 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75.0,522,1,0.0019157088122605363,10,8291.15,22410.0,73.65695728111281,184.53769252148646,2022.6691664917896 16 | 80.0,557,1,0.0017953321364452424,10,9234.36,23630.0,81.18201414816102,193.77959617524814,2155.7541415033606 17 | 85.0,592,1,0.0016891891891891893,10,10093.33,24930.0,87.89507605897444,203.54271126863466,2289.6708156932 18 | 90.0,627,1,0.001594896331738437,10,10955.92,26310.0,94.61307812892893,212.77796703819868,2423.7653272325474 19 | 95.0,662,1,0.0015105740181268882,10,11784.42,27730.0,101.08403506544332,221.818579945635,2558.2269107661577 20 | 100.0,696,1,0.0014367816091954023,10,12551.06,29170.0,107.01924521366313,231.46263092974604,2689.2792359914674 21 | 105.0,731,1,0.0013679890560875513,10,13342.64,30670.0,113.17457224203702,240.8272800887677,2824.159542410646 22 | 110.0,766,1,0.0013054830287206266,10,14172.61,32050.0,119.70935630135456,250.3915984455351,2958.424822919809 23 | 115.0,801,1,0.0012484394506866417,10,14989.29,33470.0,126.29581412493968,259.5758684179073,3092.7937722461284 24 | 120.0,836,1,0.0011961722488038277,10,15872.22,34770.0,133.35743939268215,267.9440757073786,3226.496070339927 25 | 125.0,870,1,0.0011494252873563218,10,16693.87,36130.0,139.73608218395037,277.61031709002856,3356.989625560019 26 | 130.0,905,1,0.0011049723756906078,10,17534.44,37470.0,146.2276020858468,286.40004814908934,3491.252307079679 27 | 135.0,940,1,0.0010638297872340426,10,18332.73,38970.0,152.41961764197907,295.65832229840424,3626.0950709879417 28 | 140.0,975,1,0.0010256410256410256,10,19129.95,40470.0,158.72162761641786,304.2801419831317,3760.904329552916 29 | -------------------------------------------------------------------------------- /userNum/USER_DuelingDQN10_mec2_650.data: -------------------------------------------------------------------------------- 1 | TIMESLOT,Throughout,Failure,Failurerate,User,mecAge,Run,commTotal,commEnergy,reward 2 | 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95.0,896,1,0.0011160714285714285,10,12742.52,67280.0,72.48698143627057,156.11817392193097,5318.732091013058 20 | 100.0,943,1,0.0010604453870625664,10,13533.02,70840.0,76.66786495507041,168.1994583053287,5597.8497582354075 21 | 105.0,991,1,0.0010090817356205853,10,14344.65,74430.0,81.11107827588363,177.6013467570282,5882.757439310854 22 | 110.0,1038,1,0.0009633911368015414,10,15227.13,77890.0,85.90051494741299,189.29399764016975,6161.406279261581 23 | 115.0,1085,1,0.0009216589861751152,10,15975.6,81400.0,90.2658151342864,204.6346846413674,6440.365630447875 24 | 120.0,1132,1,0.0008833922261484099,10,16714.56,84950.0,94.62453334773811,215.98788348775264,6719.370478296477 25 | 125.0,1179,1,0.0008481764206955047,10,17392.54,88510.0,98.88869766629904,233.5711079907469,6998.442576828732 26 | 130.0,1226,1,0.0008156606851549756,10,18165.09,91870.0,103.8531879935555,242.90067427395587,7275.403978821014 27 | 135.0,1274,1,0.0007849293563579278,10,18849.88,95440.0,108.58905775198131,248.96165159381175,7560.11915474752 28 | 140.0,1321,1,0.000757002271006813,10,19466.86,98970.0,113.04165778120078,255.21380944089597,7839.038635674223 29 | -------------------------------------------------------------------------------- /userNum/USER_DuelingDQN10_mec3_850.data: -------------------------------------------------------------------------------- 1 | TIMESLOT,Throughout,Failure,Failurerate,User,mecAge,Run,commTotal,commEnergy,reward 2 | 10.0,82,1,0.012195121951219513,10,203.82,7680.0,0.8386473604265886,46.95236311899443,646.7272497751879 3 | 15.0,122,1,0.00819672131147541,10,443.05,11570.0,1.736793162208226,61.80043805968772,965.8290773479634 4 | 20.0,163,1,0.006134969325153374,10,761.51,15410.0,3.067185467218801,74.97169122605682,1291.9849034226586 5 | 25.0,204,1,0.004901960784313725,10,1227.23,19160.0,4.881926354045857,86.44841594203686,1617.5160594594313 6 | 30.0,244,1,0.004098360655737705,10,1662.61,22760.0,6.6800215552476905,99.74962084693962,1934.181097148078 7 | 35.0,285,1,0.0035087719298245615,10,2070.01,26570.0,8.424824891262388,112.58641881806659,2259.5244562096827 8 | 40.0,326,1,0.003067484662576687,10,2490.34,30290.0,10.34056109505137,125.22248339318261,2583.6401094511803 9 | 45.0,366,1,0.00273224043715847,10,2921.63,33820.0,12.45997382947175,137.86534538389563,2898.1237757675412 10 | 50.0,407,1,0.002457002457002457,10,3288.94,37550.0,14.45606612553273,151.4647189030417,3221.6965596581763 11 | 55.0,448,1,0.002232142857142857,10,3907.01,40800.0,17.660358395272574,163.15508313737294,3537.0194477898604 12 | 60.0,488,1,0.0020491803278688526,10,4344.5,44280.0,20.112430582304043,174.88821084923367,3848.733629975267 13 | 65.0,529,1,0.001890359168241966,10,4917.45,47630.0,23.425400997717407,186.20633254274313,4163.924508987311 14 | 70.0,570,1,0.0017543859649122807,10,5279.4,51260.0,25.999997913889153,197.46171119078187,4484.482023291965 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115.0,936,1,0.0010683760683760685,10,16033.43,75340.0,89.80802667645791,269.4045855083924,7080.8218650129365 24 | 120.0,976,1,0.0010245901639344263,10,17160.84,78290.0,96.01106441436636,274.9548551521676,7371.608880097803 25 | 125.0,1017,1,0.0009832841691248771,10,18233.03,81400.0,101.8040532880385,277.2024552725208,7673.224264251726 26 | 130.0,1058,1,0.000945179584120983,10,19483.25,84270.0,108.45694024435498,285.77217980424547,7968.387733424503 27 | 135.0,1098,1,0.0009107468123861566,10,20657.39,87230.0,114.57024372483895,296.44138192548047,8259.251139465992 28 | 140.0,1139,1,0.000877963125548727,10,21856.79,90280.0,120.66993540651313,308.6149865961292,8558.290966950497 29 | -------------------------------------------------------------------------------- /userNum/USER_PRDQN10_mec0_250.data: -------------------------------------------------------------------------------- 1 | TIMESLOT,Throughout,Failure,Failurerate,User,mecAge,Run,commTotal,commEnergy,reward 2 | 10.0,81,1,0.012345679012345678,10,42.85,3480.0,0.6402725086715142,19.53746960536362,159.56193220813591 3 | 15.0,121,1,0.008264462809917356,10,153.07,5530.0,1.7541059999423958,26.93018989403578,239.26304492605806 4 | 20.0,161,1,0.006211180124223602,10,331.63,7780.0,3.5265044411969213,35.77708574289449,318.90412590221814 5 | 25.0,201,1,0.004975124378109453,10,593.83,9680.0,6.090706781173282,45.30132651629134,398.28966556418345 6 | 30.0,242,1,0.004132231404958678,10,881.75,11950.0,8.922816516846492,53.49646614994863,479.73424096065895 7 | 35.0,282,1,0.0035460992907801418,10,1295.35,13700.0,12.820219739950458,62.66437373240931,558.7761621444479 8 | 40.0,322,1,0.003105590062111801,10,1690.03,15750.0,16.406474695083542,72.09270366650007,638.0155398809698 9 | 45.0,362,1,0.0027624309392265192,10,2150.02,17700.0,20.549832326772712,81.41293310918064,717.1143731559538 10 | 50.0,402,1,0.0024875621890547263,10,2593.44,19850.0,24.57432527522376,89.31218148052015,796.3053845990571 11 | 55.0,442,1,0.0022624434389140274,10,3095.39,21950.0,29.186752533467416,98.40726532210572,875.3360525065074 12 | 60.0,483,1,0.002070393374741201,10,3802.43,23570.0,35.55395540409421,105.27556157335619,955.7548710837895 13 | 65.0,523,1,0.0019120458891013384,10,4353.28,25620.0,40.329960336173876,110.97766969355057,1034.7435144517651 14 | 70.0,563,1,0.0017761989342806395,10,4967.8,27370.0,45.52930725039308,116.71036590071044,1113.4655127525227 15 | 75.0,603,1,0.001658374792703151,10,5635.65,29120.0,51.142181795801555,125.76200835670079,1192.097194561556 16 | 80.0,643,1,0.0015552099533437014,10,6341.12,30670.0,56.98219533215676,134.51227353963276,1270.6224144146038 17 | 85.0,684,1,0.0014619883040935672,10,6920.39,32640.0,61.697591882216024,141.10415637005894,1351.5754781322412 18 | 90.0,724,1,0.0013812154696132596,10,7572.55,34490.0,66.83064126840938,147.98574458219693,1430.388529577551 19 | 95.0,764,1,0.0013089005235602095,10,8070.98,36790.0,70.88261432368877,158.06629186137374,1509.624129916492 20 | 100.0,804,1,0.0012437810945273632,10,8639.12,38740.0,75.73764490771029,166.50052215108354,1588.5458657581366 21 | 105.0,844,1,0.001184834123222749,10,9233.66,40640.0,80.8869985310977,174.37849244703946,1667.380373463536 22 | 110.0,884,1,0.0011312217194570137,10,9945.36,42340.0,86.8136156917519,182.6079269498416,1745.9429276436442 23 | 115.0,924,1,0.0010822510822510823,10,10641.99,44140.0,92.68855739670637,190.7353803764766,1824.5721144396962 24 | 120.0,965,1,0.0010362694300518134,10,11320.64,46060.0,98.28129259807291,200.2318185518888,1905.2968010840352 25 | 125.0,1005,1,0.0009950248756218905,10,11944.14,48010.0,103.50592330128838,209.63907095155022,1984.137703116615 26 | 130.0,1045,1,0.0009569377990430622,10,12600.66,49860.0,109.03356534176882,218.2436566072277,2062.87504737478 27 | 135.0,1085,1,0.0009216589861751152,10,13219.93,51910.0,114.23858824573777,226.34311847114733,2141.791369410166 28 | 140.0,1125,1,0.0008888888888888889,10,13942.07,53660.0,120.28900949958626,235.60449890743445,2220.367326917002 29 | -------------------------------------------------------------------------------- /userNum/USER_PRDQN10_mec1_450.data: -------------------------------------------------------------------------------- 1 | TIMESLOT,Throughout,Failure,Failurerate,User,mecAge,Run,commTotal,commEnergy,reward 2 | 10.0,70,1,0.014285714285714285,10,87.85,2170.0,1.2527770243233989,28.805171969757456,272.38228384631344 3 | 15.0,105,1,0.009523809523809525,10,233.74,3400.0,2.9937622169649334,39.6259400158035,409.57634341578785 4 | 20.0,140,1,0.007142857142857143,10,472.63,4670.0,5.746167885048242,52.35427431127022,545.844705314952 5 | 25.0,174,1,0.005747126436781609,10,769.01,5970.0,9.053126806701497,63.4817202780158,677.8939188036436 6 | 30.0,209,1,0.004784688995215311,10,1106.05,7360.0,12.758549997603797,72.85394504057928,813.5829126355902 7 | 35.0,244,1,0.004098360655737705,10,1556.69,8430.0,17.586048062420797,82.69592431603597,946.0925339034661 8 | 40.0,279,1,0.0035842293906810036,10,2069.21,9540.0,22.783486377338853,91.16180548359539,1078.2452137504135 9 | 45.0,314,1,0.0031847133757961785,10,2589.06,10770.0,27.928428995089774,100.6415919785272,1210.978342979492 10 | 50.0,348,1,0.0028735632183908046,10,3202.66,11710.0,33.80852778641924,109.98742908737965,1337.4180969965748 11 | 55.0,383,1,0.0026109660574412533,10,3844.92,12700.0,39.795476203459366,120.68345352283353,1467.6771368626871 12 | 60.0,418,1,0.0023923444976076554,10,4438.77,13850.0,45.21789153013254,130.86672358942081,1599.9552627577805 13 | 65.0,453,1,0.002207505518763797,10,5050.61,15080.0,51.04424526838754,139.42127949123034,1732.082951520893 14 | 70.0,488,1,0.0020491803278688526,10,5689.62,16230.0,56.961824875832754,148.06773258192425,1863.581295354883 15 | 75.0,522,1,0.0019157088122605363,10,6355.63,17330.0,62.929545672295184,154.889621205925,1990.918675670238 16 | 80.0,557,1,0.0017953321364452424,10,6995.28,18520.0,68.73896052512514,163.75734500230243,2122.6100080082992 17 | 85.0,592,1,0.0016891891891891893,10,7667.36,19670.0,74.8192395223976,174.72632357597422,2253.7046487215725 18 | 90.0,627,1,0.001594896331738437,10,8442.54,20500.0,81.64994625353073,182.5174667000822,2381.722708700816 19 | 95.0,662,1,0.0015105740181268882,10,9157.15,21610.0,88.04196608705774,192.68731028005087,2511.984120795977 20 | 100.0,696,1,0.0014367816091954023,10,9843.95,22630.0,94.11713331534057,201.89405712883666,2638.6745772780378 21 | 105.0,731,1,0.0013679890560875513,10,10538.34,23740.0,100.29507815177547,210.8290059632438,2769.5722991905386 22 | 110.0,766,1,0.0013054830287206266,10,11219.48,24770.0,106.30154630405787,219.06362959377614,2900.097476331168 23 | 115.0,801,1,0.0012484394506866417,10,11781.94,26120.0,111.44397081743067,230.05127338122534,3033.971309374911 24 | 120.0,836,1,0.0011961722488038277,10,12541.08,27110.0,118.3500363981608,238.00910024552556,3162.9372631500255 25 | 125.0,870,1,0.0011494252873563218,10,13208.33,28210.0,124.2931772342908,246.66893786918376,3290.129634387761 26 | 130.0,905,1,0.0011049723756906078,10,13885.77,29320.0,130.36547402414467,256.9816038334164,3421.1037945488993 27 | 135.0,940,1,0.0010638297872340426,10,14669.98,30230.0,137.37308506072458,267.23484415211334,3549.3893120366824 28 | 140.0,975,1,0.0010256410256410256,10,15391.7,31380.0,143.7551298333709,274.3215769385173,3680.1212747757672 29 | -------------------------------------------------------------------------------- /userNum/USER_PRDQN10_mec2_650.data: -------------------------------------------------------------------------------- 1 | TIMESLOT,Throughout,Failure,Failurerate,User,mecAge,Run,commTotal,commEnergy,reward 2 | 10.0,95,1,0.010526315789473684,10,128.48,5920.0,1.360000744177955,25.92080645699651,561.7390452317956 3 | 15.0,142,1,0.007042253521126761,10,331.54,9020.0,3.3382400947577677,37.17320081814403,841.6682026525411 4 | 20.0,189,1,0.005291005291005291,10,648.95,12060.0,6.2074834199063345,47.9581611164226,1120.7147751906268 5 | 25.0,236,1,0.00423728813559322,10,1014.55,15220.0,9.489074306372377,57.53374477048493,1399.7148635429319 6 | 30.0,283,1,0.0035335689045936395,10,1479.69,18300.0,13.460593101307149,66.9815247157978,1677.8422163897387 7 | 35.0,331,1,0.0030211480362537764,10,2042.4,21260.0,18.071713081921597,78.3793830677378,1961.2194094928786 8 | 40.0,378,1,0.0026455026455026454,10,2604.65,24280.0,22.724293267433993,89.0345342926571,2238.7220884585486 9 | 45.0,425,1,0.002352941176470588,10,3230.44,27300.0,27.73832291747807,98.78208654260509,2515.7301730673043 10 | 50.0,472,1,0.00211864406779661,10,3882.76,30220.0,33.16288906551216,108.31117925854993,2792.4964110128067 11 | 55.0,519,1,0.0019267822736030828,10,4772.67,33020.0,40.15848799720116,117.88055243900223,3067.4571836913024 12 | 60.0,566,1,0.0017667844522968198,10,5470.72,36040.0,45.55009909345738,126.86887582732012,3344.2530845409246 13 | 65.0,613,1,0.0016313213703099511,10,6201.87,38960.0,51.23654387614616,134.4632241223951,3620.5648824325413 14 | 70.0,661,1,0.0015128593040847202,10,6951.37,41980.0,57.05310353445855,143.6835547657388,3902.748563565677 15 | 75.0,708,1,0.0014124293785310734,10,7924.07,44760.0,64.47936155268057,152.43298966124416,4177.105016272988 16 | 80.0,755,1,0.0013245033112582781,10,8706.78,47700.0,70.20439703440046,162.45288999304634,4453.280588020566 17 | 85.0,802,1,0.0012468827930174563,10,9569.72,50580.0,76.6256705058612,172.66076240776152,4728.709466505331 18 | 90.0,849,1,0.001177856301531213,10,10510.17,53340.0,83.6192970685744,179.98865768184285,5003.437318925282 19 | 95.0,896,1,0.0011160714285714285,10,11313.44,56280.0,89.47956775071884,192.37404163677644,5279.596233427663 20 | 100.0,944,1,0.001059322033898305,10,12202.6,59260.0,96.14147474035448,200.44136845459124,5560.998105265655 21 | 105.0,991,1,0.0010090817356205853,10,12963.79,62220.0,101.91849042249378,209.43359677112386,5837.5115363932655 22 | 110.0,1038,1,0.0009633911368015414,10,13741.02,65160.0,107.77040189326594,219.96526004053243,6113.631279210754 23 | 115.0,1085,1,0.0009216589861751152,10,14700.89,67960.0,114.89830516737103,227.00555894276317,6388.308271161444 24 | 120.0,1132,1,0.0008833922261484099,10,15538.61,70860.0,121.10522484280746,236.67836324990344,6664.197104475728 25 | 125.0,1179,1,0.0008481764206955047,10,16312.57,73880.0,126.83658384340254,244.8438709864505,6940.659017227205 26 | 130.0,1227,1,0.0008149959250203749,10,17208.52,76840.0,133.54584664293967,252.42789329154922,7221.758624809442 27 | 135.0,1274,1,0.0007849293563579278,10,18121.55,79660.0,140.35705102194083,260.998820557683,7496.715091862646 28 | 140.0,1321,1,0.000757002271006813,10,19009.12,82520.0,146.89203476283504,267.97571018677957,7772.057481882624 29 | -------------------------------------------------------------------------------- /userNum/USER_PRDQN10_mec3_850.data: -------------------------------------------------------------------------------- 1 | TIMESLOT,Throughout,Failure,Failurerate,User,mecAge,Run,commTotal,commEnergy,reward 2 | 10.0,82,1,0.012195121951219513,10,87.88,6840.0,0.5687930731812132,23.58334321273353,646.1105884174184 3 | 15.0,122,1,0.00819672131147541,10,279.94,10220.0,1.3854978043959678,32.24941621345481,964.4784902976038 4 | 20.0,163,1,0.006134969325153374,10,486.97,14020.0,2.266125316845852,39.02149916401969,1291.2597243551959 5 | 25.0,204,1,0.004901960784313725,10,791.41,17900.0,3.4761493695285846,47.4660132365702,1618.028691709127 6 | 30.0,244,1,0.004098360655737705,10,1173.74,21490.0,4.912657891127557,55.441976694885604,1935.9942433317076 7 | 35.0,285,1,0.0035087719298245615,10,1479.14,25270.0,6.1902641494112505,63.54199249570237,2262.099653164731 8 | 40.0,326,1,0.003067484662576687,10,1915.29,28780.0,8.118877849188786,70.26318370383423,2586.9772683322385 9 | 45.0,366,1,0.00273224043715847,10,2382.1,32170.0,10.111850473054568,77.39295811653851,2903.4241069800782 10 | 50.0,407,1,0.002457002457002457,10,2774.66,35750.0,11.854381707365484,84.10749670343633,3228.031031244644 11 | 55.0,448,1,0.002232142857142857,10,3220.21,39280.0,13.94286942051858,91.58691531547737,3552.4771419261315 12 | 60.0,488,1,0.0020491803278688526,10,3685.14,42630.0,16.121468535734245,99.59391668366261,3868.147754784704 13 | 65.0,529,1,0.001890359168241966,10,4141.74,46220.0,18.327621714758948,106.54517321243588,4192.011941665694 14 | 70.0,570,1,0.0017543859649122807,10,4896.52,49380.0,21.794857770684757,114.26440755018857,4512.290664600499 15 | 75.0,610,1,0.001639344262295082,10,5480.67,52740.0,24.5297980021729,120.68686000946344,4826.596384843947 16 | 80.0,651,1,0.0015360983102918587,10,6108.35,56200.0,27.54982358817516,127.47713494379732,5148.431822995621 17 | 85.0,692,1,0.001445086705202312,10,7021.6,59230.0,31.841424443724954,134.32956758361118,5466.464023195004 18 | 90.0,732,1,0.001366120218579235,10,7696.41,62610.0,34.96505106357448,140.19254194353704,5779.848896576736 19 | 95.0,773,1,0.00129366106080207,10,8545.98,65820.0,38.97494224447923,146.60737748603555,6099.083801961084 20 | 100.0,814,1,0.0012285012285012285,10,9468.99,68940.0,43.25242731954603,153.83420166511587,6416.846856292697 21 | 105.0,854,1,0.00117096018735363,10,10806.32,71700.0,49.293293932426316,159.73837487437652,6722.401764035976 22 | 110.0,895,1,0.0011173184357541898,10,12180.91,74500.0,55.401780334995436,165.98170068805481,7035.614207501032 23 | 115.0,936,1,0.0010683760683760685,10,13075.91,77790.0,59.33523345121159,172.902491049713,7355.137364676155 24 | 120.0,976,1,0.0010245901639344263,10,14328.91,80660.0,64.90007042376669,179.51778318880233,7662.045349578092 25 | 125.0,1017,1,0.0009832841691248771,10,15185.94,83950.0,68.6856327262737,188.2745201404754,7981.463106255431 26 | 130.0,1058,1,0.000945179584120983,10,16079.93,87200.0,72.80201806617737,194.75342338536691,8300.380308469454 27 | 135.0,1098,1,0.0009107468123861566,10,17470.49,89940.0,78.99397252167041,202.3332513954363,8605.2828022103 28 | 140.0,1139,1,0.000877963125548727,10,18469.04,93130.0,83.52483402840294,209.41812891923087,8923.612958074285 29 | -------------------------------------------------------------------------------- /userNum/USER_offline3_10_mec0_250.data: -------------------------------------------------------------------------------- 1 | TIMESLOT,Throughout,Failure,Failurerate,User,mecAge,Run,commTotal,commEnergy,reward 2 | 10.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 3 | 15.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 4 | 20.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 5 | 25.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 6 | 30.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 7 | 35.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 8 | 40.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 9 | 45.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 10 | 50.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 11 | 55.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 12 | 60.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 13 | 65.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 14 | 70.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 15 | 75.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 16 | 80.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 17 | 85.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 18 | 90.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 19 | 95.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 20 | 100.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 21 | 105.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 22 | 110.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 23 | 115.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 24 | 120.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 25 | 125.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 26 | 130.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 27 | 135.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 28 | 140.0,13,1,0.07692307692307693,3,8.48,1040.0,0.020144571353096597,0.12977020790278845,23.993811990194214 29 | -------------------------------------------------------------------------------- /userNum/USER_offline3_10_mec1_450.data: -------------------------------------------------------------------------------- 1 | TIMESLOT,Throughout,Failure,Failurerate,User,mecAge,Run,commTotal,commEnergy,reward 2 | 10.0,70,1,0.014285714285714285,3,2.81,5540.0,0.1474870402637431,2.813232721321154,275.81029622805727 3 | 15.0,105,1,0.009523809523809525,3,4.26,8340.0,0.22302918283785583,4.203627805993873,415.71303618707117 4 | 20.0,140,1,0.007142857142857143,3,5.65,11140.0,0.29857132541196857,5.594022890666631,555.6157761460856 5 | 25.0,174,1,0.005747126436781609,3,6.98,13860.0,0.3719551210553924,6.9844179753393885,691.5212949634154 6 | 30.0,209,1,0.004784688995215311,3,8.43,16660.0,0.44749726362950515,8.374813060012123,831.4240349224312 7 | 35.0,244,1,0.004098360655737705,3,9.78,19460.0,0.5230394062036179,9.765208144684792,971.3267748814471 8 | 40.0,279,1,0.0035842293906810036,3,21.13,22260.0,0.6337991758625051,11.650075509749776,1111.22045769462 9 | 45.0,314,1,0.0031847133757961785,3,127.57,25060.0,1.2257913766578983,18.35633067713873,1250.9904903794593 10 | 50.0,348,1,0.0028735632183908046,3,332.39,27780.0,2.383746474043247,25.756183082153385,1386.6176070374609 11 | 55.0,383,1,0.0026109660574412533,3,664.34,30580.0,4.105496553089786,32.17391336978305,1526.0981772775224 12 | 60.0,418,1,0.0023923444976076554,3,1106.69,33380.0,6.267423009293846,38.01303425560817,1665.4663013822515 13 | 65.0,453,1,0.002207505518763797,3,1639.58,36180.0,8.690731052779418,43.60513838293557,1804.7677577885102 14 | 70.0,488,1,0.0020491803278688526,3,2255.66,38980.0,11.363098095582279,48.98180622354989,1944.0057355593055 15 | 75.0,522,1,0.0019157088122605363,3,2925.34,41700.0,14.124629740242442,53.89979248610229,2079.2232948899195 16 | 80.0,557,1,0.0017953321364452424,3,3693.34,44500.0,17.170333647722742,58.55422342081805,2218.3662197397525 17 | 85.0,592,1,0.0016891891891891893,3,4497.58,47300.0,20.23407539692806,63.32867523268091,2357.504540585675 18 | 90.0,627,1,0.001594896331738437,3,5321.81,50100.0,23.292397449116205,67.6877038411323,2496.644203316598 19 | 95.0,662,1,0.0015105740181268882,3,6213.05,52900.0,26.500663354471463,72.18702299921708,2635.7457246900226 20 | 100.0,696,1,0.0014367816091954023,3,7061.52,55620.0,29.50120185762589,77.00130372348501,2770.902449569805 21 | 105.0,731,1,0.0013679890560875513,3,8005.11,58420.0,32.77696860091697,81.49842641814928,2909.9867947578327 22 | 110.0,766,1,0.0013054830287206266,3,8934.35,61220.0,35.98452681027747,86.22476719135108,3049.088502145399 23 | 115.0,801,1,0.0012484394506866417,3,9857.34,64020.0,39.127053190128414,90.70375974734382,3188.20673107259 24 | 120.0,836,1,0.0011961722488038277,3,10806.02,66820.0,42.337297220882185,95.20316624545957,3327.3077389937184 25 | 125.0,870,1,0.0011494252873563218,3,11700.33,69540.0,45.40407039851119,99.62577661272968,3462.447593935047 26 | 130.0,905,1,0.0011049723756906078,3,12646.42,72340.0,48.71302242925374,104.29464567078068,3601.523499580709 27 | 135.0,940,1,0.0010638297872340426,3,13598.66,75140.0,51.98569977162674,108.74715919894031,3740.6086347092605 28 | 140.0,975,1,0.0010256410256410256,3,14521.34,77940.0,55.161022650294846,113.22984628792516,3879.718530716427 29 | -------------------------------------------------------------------------------- /userNum/USER_offline3_10_mec2_650.data: -------------------------------------------------------------------------------- 1 | TIMESLOT,Throughout,Failure,Failurerate,User,mecAge,Run,commTotal,commEnergy,reward 2 | 10.0,95,1,0.010526315789473684,3,3.73,8470.0,0.13453695867960958,1.9372838179773382,563.6537269585796 3 | 15.0,142,1,0.007042253521126761,3,5.65,12700.0,0.20216516250786198,2.864213874425842,845.4795717277797 4 | 20.0,189,1,0.005291005291005291,3,7.51,16930.0,0.26979336633611445,3.791143930874347,1127.3054164969797 5 | 25.0,236,1,0.00423728813559322,3,9.4,21160.0,0.33742157016436686,4.718073987322851,1409.1312612661798 6 | 30.0,283,1,0.0035335689045936395,3,11.32,25390.0,0.40504977399261927,5.6450040437713565,1690.9571060353799 7 | 35.0,330,1,0.0030303030303030303,3,13.16,29620.0,0.47267797782087173,6.5719341002198615,1972.78295080458 8 | 40.0,378,1,0.0026455026455026454,3,15.08,33940.0,0.541745079602918,7.4988641566683665,2260.6050901433373 9 | 45.0,425,1,0.002352941176470588,3,16.98,38170.0,0.609373283431173,8.425794213116871,2542.4309349125374 10 | 50.0,472,1,0.00211864406779661,3,47.53,42400.0,0.7753730545929612,10.002574916060937,2824.2061810688656 11 | 55.0,519,1,0.0019267822736030828,3,195.34,46630.0,1.4262003051217331,14.478499995835346,3105.7322762565364 12 | 60.0,566,1,0.0017667844522968198,3,450.88,50860.0,2.605564471723908,19.930901814222523,3386.9872414452498 13 | 65.0,613,1,0.0016313213703099511,3,861.81,55090.0,4.305427504858164,24.59678313066904,3667.9757346641168 14 | 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| 115.0,1085,1,0.0009216589861751152,3,8389.59,97570.0,29.317630541871633,65.00690322109875,6485.762617977382 24 | 120.0,1132,1,0.0008833922261484099,3,9219.03,101800.0,31.896739188950942,68.80697678607613,6766.302170614562 25 | 125.0,1179,1,0.0008481764206955047,3,10144.25,106030.0,34.74840786315057,72.74640495366721,7046.702802458105 26 | 130.0,1226,1,0.0008156606851549756,3,11013.38,110260.0,37.42064002617891,76.48236976474712,7327.194865786171 27 | 135.0,1274,1,0.0007849293563579278,3,11889.39,114580.0,40.09299795668778,80.56417401959447,7613.683792565072 28 | 140.0,1321,1,0.000757002271006813,3,12799.29,118810.0,42.87216313783992,84.37603500058275,7894.121345671745 29 | -------------------------------------------------------------------------------- /userNum/USER_offline3_10_mec3_850.data: -------------------------------------------------------------------------------- 1 | TIMESLOT,Throughout,Failure,Failurerate,User,mecAge,Run,commTotal,commEnergy,reward 2 | 10.0,82,1,0.012195121951219513,3,3.3,8100.0,0.058275367128600944,1.0427963135045488,647.7598287781389 3 | 15.0,122,1,0.00819672131147541,3,4.9,12100.0,0.08705332620445308,1.5062613417288013,967.6412257056149 4 | 20.0,163,1,0.006134969325153374,3,6.55,16200.0,0.11655073425720149,1.9697263699530538,1295.519657556278 5 | 25.0,204,1,0.004901960784313725,3,8.2,20300.0,0.1460481423099499,2.433191398177306,1623.398089406941 6 | 30.0,244,1,0.004098360655737705,3,9.79,24300.0,0.17482610138580199,2.8966564264015586,1943.279486334417 7 | 35.0,285,1,0.0035087719298245615,3,11.38,28400.0,0.20432350943855038,3.360121454625811,2271.157918185074 8 | 40.0,326,1,0.003067484662576687,3,13.03,32500.0,0.2338209174912988,3.8235864828500636,2599.0363500357275 9 | 45.0,366,1,0.00273224043715847,3,46.17,36500.0,0.3133173009726885,4.751689542979177,2918.875981159051 10 | 50.0,407,1,0.002457002457002457,3,195.04,40600.0,0.6131580257495783,7.255161504311695,3246.5318908684285 11 | 55.0,448,1,0.002232142857142857,3,469.28,44700.0,1.190699834520646,9.684758613317626,3573.9595394922185 12 | 60.0,488,1,0.0020491803278688526,3,848.81,48700.0,1.9021362471217855,11.860209011710888,3893.2796394850648 13 | 65.0,529,1,0.001890359168241966,3,1307.28,52800.0,2.7304118895647793,14.012495110958653,4220.501464199644 14 | 70.0,570,1,0.0017543859649122807,3,1931.36,56900.0,3.7266883598621425,15.846142826934845,4547.585512168331 15 | 75.0,610,1,0.001639344262295082,3,2597.68,60900.0,4.800748007705111,17.757144012755983,4866.608276074059 16 | 80.0,651,1,0.0015360983102918587,3,3331.1,65000.0,5.950941335074827,19.508020701718383,5193.56622931497 17 | 85.0,692,1,0.001445086705202312,3,4127.97,69100.0,7.095184671450455,21.357191230162805,5520.529057197591 18 | 90.0,732,1,0.001366120218579235,3,4893.83,73100.0,8.150698405289909,23.142510323526892,5839.566973335751 19 | 95.0,773,1,0.00129366106080207,3,5782.44,77200.0,9.381011884124595,24.92237642814028,6166.459298700634 20 | 100.0,814,1,0.0012285012285012285,3,6536.66,81300.0,10.482608908645956,26.672546235442397,6493.457023771828 21 | 105.0,854,1,0.00117096018735363,3,7504.81,85300.0,11.769650632539772,28.458819272017006,6812.305354267973 22 | 110.0,895,1,0.0011173184357541898,3,8287.11,89400.0,12.873004954067087,30.193256477301876,7139.301642586618 23 | 115.0,936,1,0.0010683760683760685,3,9291.82,93500.0,14.163366095062544,32.016775406938095,7466.144799530967 24 | 120.0,976,1,0.0010245901639344263,3,10144.91,97500.0,15.32719315102008,33.65261621829687,7785.094019908348 25 | 125.0,1017,1,0.0009832841691248771,3,11074.01,101600.0,16.568099733684406,35.414827747994046,8111.977669665156 26 | 130.0,1058,1,0.000945179584120983,3,11978.71,105700.0,17.78407490270237,37.006721739818225,8438.881745627124 27 | 135.0,1098,1,0.0009107468123861566,3,12896.94,109700.0,18.993848862575444,38.790731910995966,8757.793342599614 28 | 140.0,1139,1,0.000877963125548727,3,13852.83,113800.0,20.279633956684304,40.37990237575874,9084.640270699221 29 | -------------------------------------------------------------------------------- /userNum/USER_online3_10_mec0_250.data: -------------------------------------------------------------------------------- 1 | TIMESLOT,Throughout,Failure,Failurerate,User,mecAge,Run,commTotal,commEnergy,reward 2 | 10.0,81,1,0.012345679012345678,10,4.39,2620.0,0.6077559102804599,8.767629177577309,159.55087842614626 3 | 15.0,121,1,0.008264462809917356,10,6.66,3920.0,0.923071081172531,12.924839192127262,239.32823744236958 4 | 20.0,161,1,0.006211180124223602,10,8.86,5140.0,1.2331485557593016,17.307752229286574,319.0863519074314 5 | 25.0,201,1,0.004975124378109453,10,10.82,6420.0,1.5501218805346269,21.5038889525852,398.85759980746695 6 | 30.0,242,1,0.004132231404958678,10,13.25,7740.0,1.8740165125119816,25.948428474332424,480.62562060225673 7 | 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