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Sample application 5 used in IEEE Transactions on Intelligent Transportation Systems is added.

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This commit is contained in:
Cagatay Sonmez
2020-10-30 12:57:07 +03:00
parent 08341c3681
commit 6660de75b2
62 changed files with 6084 additions and 5 deletions
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scripts/sample_app5/.gitignore vendored Normal file
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output

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scripts/sample_app5/ai_trainer/.gitignore vendored Normal file
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*.class

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scripts/sample_app5/ai_trainer/README.md Normal file
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# Configure Simulation Settings
Firstly, edit config.json file in a way to declare the configuration of the simulation process used to collect training data
# Preparing Training Data
Invoke following command to convert collected data format for weka models
```
./generate_training_data.sh
```
This command creates *.arff files under the simulation results folder
# Generating Classification and Regression Models
Invoke following command to generate related weka models
```
./generate_weka_models.sh
```
This script creates weka model files under the simulation results folder. When you are done with training, you can move these files to ../config/weka/ folder

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scripts/sample_app5/ai_trainer/WekaModelCreator.java Normal file
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import java.io.FileReader;
import java.text.DateFormat;
import java.text.SimpleDateFormat;
import java.util.Calendar;
import java.util.Date;
import java.util.concurrent.TimeUnit;
import org.json.simple.JSONObject;
import org.json.simple.parser.JSONParser;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.functions.LinearRegression;
import weka.classifiers.functions.MultilayerPerceptron;
import weka.classifiers.functions.SMO;
import weka.classifiers.functions.SMOreg;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
public class WekaModelCreator {
private static final String[] targets = {"edge","cloud_rsu","cloud_gsm"};
public static void main(String[] args) throws Exception {
String dataPath = "";
String classifier = "";
String regressor = "";
JSONParser parser = new JSONParser();
try
{
Object object = parser.parse(new FileReader(args[0]));
//convert Object to JSONObject
JSONObject jsonObject = (JSONObject)object;
//Reading the String
dataPath = (String) jsonObject.get("sim_result_folder");
classifier = (String) jsonObject.get("classifier");
regressor = (String) jsonObject.get("regressor");
}
catch(Exception e)
{
e.printStackTrace();
System.exit(1);
}
System.out.println("######### TRAINING FOR " + dataPath + " #########");
for(int i=0; i<targets.length; i++) {
handleClassify("train", targets[i], classifier, dataPath);
handleRegression("train", targets[i], regressor, dataPath);
}
System.out.println("######### EVALUATION FOR " + dataPath + " #########");
for(int i=0; i<targets.length; i++) {
handleClassify("evaluate", targets[i], classifier, dataPath);
handleRegression("evaluate", targets[i], regressor, dataPath);
}
}
public static void handleRegression(String action, String target, String method, String dataFolder) throws Exception {
if(action.equals("train")) {
DateFormat df = new SimpleDateFormat("dd/MM/yyyy HH:mm:ss");
Date startDate = Calendar.getInstance().getTime();
String now = df.format(startDate);
System.out.println("Training " + method + " for " + target + " started at " + now);
DataSource edgeRegressionSource = new DataSource(dataFolder + "/" + target + "_regression_train.arff");
Instances edgeRegressionDataset = edgeRegressionSource.getDataSet();
edgeRegressionDataset.setClassIndex(edgeRegressionDataset.numAttributes()-1);
if(method.equals("LinearRegression")) {
LinearRegression lr = new LinearRegression();
lr.buildClassifier(edgeRegressionDataset);
weka.core.SerializationHelper.write(dataFolder + "/lr_" + target + ".model", lr);
}
else if(method.equals("SMOreg")) {
SMOreg smoreg = new SMOreg();
smoreg.buildClassifier(edgeRegressionDataset);
weka.core.SerializationHelper.write(dataFolder + "/smoreg_" + target + ".model", smoreg);
}
Date endDate = Calendar.getInstance().getTime();
now = df.format(endDate);
System.out.println("Training " + method + " for " + target + " fisished at " + now + ". It took " + getTimeDifference(startDate, endDate));
}
else if(action.equals("evaluate")) {
System.out.println("Evaluation " + method + " for " + target + " started");
DataSource edgeRegressionSource = new DataSource(dataFolder + "/" + target + "_regression_test.arff");
Instances edgeRegressionDataset = edgeRegressionSource.getDataSet();
edgeRegressionDataset.setClassIndex(edgeRegressionDataset.numAttributes()-1);
if(method.equals("LinearRegression")) {
LinearRegression lr = (LinearRegression) weka.core.SerializationHelper.read(dataFolder + "/lr_" + target + ".model");
Evaluation lrEval = new Evaluation(edgeRegressionDataset);
lrEval.evaluateModel(lr, edgeRegressionDataset);
System.out.println("LinearRegression");
System.out.println(lrEval.toSummaryString());
}
else if(method.equals("SMOreg")) {
SMOreg smoreg = (SMOreg) weka.core.SerializationHelper.read(dataFolder + "/smoreg_" + target + ".model");
Evaluation svmregEval = new Evaluation(edgeRegressionDataset);
svmregEval.evaluateModel(smoreg, edgeRegressionDataset);
System.out.println("SMOreg");
System.out.println(svmregEval.toSummaryString());
}
System.out.println("Evaluation " + method + " for " + target + " fisished");
System.out.println("");
}
}
public static void handleClassify(String action, String target, String method, String dataFolder) throws Exception {
if(action.equals("train")) {
DateFormat df = new SimpleDateFormat("dd/MM/yyyy HH:mm:ss");
Date startDate = Calendar.getInstance().getTime();
String now = df.format(startDate);
System.out.println("Training " + method + " for " + target + " started at " + now);
DataSource classifierSource = new DataSource(dataFolder + "/" + target + "_classifier_train.arff");
Instances classifierDataset = classifierSource.getDataSet();
classifierDataset.setClassIndex(classifierDataset.numAttributes()-1);
if(method.equals("NaiveBayes")) {
NaiveBayes nb = new NaiveBayes();
nb.buildClassifier(classifierDataset);
weka.core.SerializationHelper.write(dataFolder + "/nb_" + target + ".model", nb);
}
else if(method.equals("SMO")) {
SMO smo = new SMO();
smo.buildClassifier(classifierDataset);
weka.core.SerializationHelper.write(dataFolder + "/smo_" + target + ".model", smo);
}
else if(method.equals("MultilayerPerceptron")) {
MultilayerPerceptron mlp = new MultilayerPerceptron();
mlp.setLearningRate(0.1);
//mlp.setMomentum(0.2);
mlp.setTrainingTime(1000);
//mlp.setHiddenLayers("3");
mlp.buildClassifier(classifierDataset);
weka.core.SerializationHelper.write(dataFolder + "/mlp_" + target + ".model", mlp);
}
Date endDate = Calendar.getInstance().getTime();
now = df.format(endDate);
System.out.println("Training " + method + " for " + target + " fisished at " + now + ". It took " + getTimeDifference(startDate, endDate));
}
else if(action.equals("evaluate")) {
System.out.println("Evaluation " + method + " for " + target + " started");
DataSource edgeClassifierSource = new DataSource(dataFolder + "/" + target + "_classifier_test.arff");
Instances classifierDataset = edgeClassifierSource.getDataSet();
classifierDataset.setClassIndex(classifierDataset.numAttributes()-1);
if(method.equals("NaiveBayes")) {
NaiveBayes nb = (NaiveBayes) weka.core.SerializationHelper.read(dataFolder + "/nb_" + target + ".model");
Evaluation nbEval = new Evaluation(classifierDataset);
nbEval.evaluateModel(nb, classifierDataset);
System.out.println(nbEval.toSummaryString());
System.out.println(nbEval.toMatrixString());
System.out.println(nbEval.toClassDetailsString());
}
else if(method.equals("SMO")) {
SMO smo = (SMO) weka.core.SerializationHelper.read(dataFolder + "/smo_" + target + ".model");
Evaluation smoEval = new Evaluation(classifierDataset);
smoEval.evaluateModel(smo, classifierDataset);
System.out.println(smoEval.toSummaryString());
System.out.println(smoEval.toMatrixString());
System.out.println(smoEval.toClassDetailsString());
}
else if(method.equals("MultilayerPerceptron")) {
MultilayerPerceptron mlp = (MultilayerPerceptron) weka.core.SerializationHelper.read(dataFolder + "/mlp_" + target + ".model");
Evaluation mlpEval = new Evaluation(classifierDataset);
mlpEval.evaluateModel(mlp, classifierDataset);
System.out.println(mlpEval.toSummaryString());
System.out.println(mlpEval.toMatrixString());
System.out.println(mlpEval.toClassDetailsString());
}
System.out.println("Evaluation " + method + " for " + target + " fisished");
System.out.println("");
}
}
private static String getTimeDifference(Date startDate, Date endDate){
String result = "";
long duration = endDate.getTime() - startDate.getTime();
long diffInMilli = TimeUnit.MILLISECONDS.toMillis(duration);
long diffInSeconds = TimeUnit.MILLISECONDS.toSeconds(duration);
long diffInMinutes = TimeUnit.MILLISECONDS.toMinutes(duration);
long diffInHours = TimeUnit.MILLISECONDS.toHours(duration);
long diffInDays = TimeUnit.MILLISECONDS.toDays(duration);
if(diffInDays>0)
result += diffInDays + ((diffInDays>1 == true) ? " Days " : " Day ");
if(diffInHours>0)
result += diffInHours % 24 + ((diffInHours>1 == true) ? " Hours " : " Hour ");
if(diffInMinutes>0)
result += diffInMinutes % 60 + ((diffInMinutes>1 == true) ? " Minutes " : " Minute ");
if(diffInSeconds>0)
result += diffInSeconds % 60 + ((diffInSeconds>1 == true) ? " Seconds" : " Second");
if(diffInMilli>0 && result.isEmpty())
result += diffInMilli + ((diffInMilli>1 == true) ? " Milli Seconds" : " Milli Second");
return result;
}
}

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{
"sim_result_folder": "/home/cagatay/Projects/git-repos/EdgeCloudSim/scripts/sample_app5/output/18-10-2019_21-16/default_config",
"num_iterations": 10,
"train_data_ratio": 80,
"min_vehicle": 100,
"max_vehicle": 2200,
"vehicle_step_size": 100,
"classifier": "MultilayerPerceptron",
"regressor": "LinearRegression"
}

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import pandas as pd
import json
import sys
if len (sys.argv) != 5:
print('invalid arguments. Usage:')
print('python data_conventor.py config.json [edge|cloud_rsu|cloud_gsm] [classifier|regression] [train|test]')
sys.exit(1)
with open(sys.argv[1]) as json_data_file:
data = json.load(json_data_file)
target = sys.argv[2]
method = sys.argv[3]
datatype = sys.argv[4]
print("conversion started with args " + target + ", " + method + ", " + datatype)
sim_result_folder = data["sim_result_folder"]
num_iterations = data["num_iterations"]
train_data_ratio = data["train_data_ratio"]
min_vehicle = data["min_vehicle"]
max_vehicle = data["max_vehicle"]
vehicle_step_size = data["vehicle_step_size"]
def getDecisionColumnName(target):
if target == "edge":
COLUMN_NAME = "EDGE"
elif target == "cloud_rsu":
COLUMN_NAME = "CLOUD_VIA_RSU"
elif target == "cloud_gsm":
COLUMN_NAME = "CLOUD_VIA_GSM"
return COLUMN_NAME
def getClassifierColumns(target):
if target == "edge":
result = ["NumOffloadedTask", "TaskLength", "WLANUploadDelay", "WLANDownloadDelay", "AvgEdgeUtilization", "Result"]
elif target == "cloud_rsu":
result = ["NumOffloadedTask", "WANUploadDelay", "WANDownloadDelay", "Result"]
elif target == "cloud_gsm":
result = ["NumOffloadedTask", "GSMUploadDelay", "GSMDownloadDelay", "Result"]
return result
def getRegressionColumns(target):
if target == "edge":
result = ["TaskLength", "AvgEdgeUtilization", "ServiceTime"]
elif target == "cloud_rsu":
result = ["TaskLength", "WANUploadDelay", "WANDownloadDelay", "ServiceTime"]
elif target == "cloud_gsm":
result = ["TaskLength", "GSMUploadDelay", "GSMDownloadDelay", "ServiceTime"]
return result
def znorm(column):
column = (column - column.mean()) / column.std()
return column
data_set = []
testDataStartIndex = (train_data_ratio * num_iterations) / 100
for ite in range(num_iterations):
for vehicle in range(min_vehicle, max_vehicle+1, vehicle_step_size):
if (datatype == "train" and ite < testDataStartIndex) or (datatype == "test" and ite >= testDataStartIndex):
file_name = sim_result_folder + "/ite" + str(ite + 1) + "/" + str(vehicle) + "_learnerOutputFile.cvs"
df = [pd.read_csv(file_name, na_values = "?", comment='\t', sep=",")]
df[0]['VehicleCount'] = vehicle
#print(file_name)
data_set += df
data_set = pd.concat(data_set, ignore_index=True)
data_set = data_set[data_set['Decision'] == getDecisionColumnName(target)]
if method == "classifier":
targetColumns = getClassifierColumns(target)
else:
targetColumns= getRegressionColumns(target)
if datatype == "train":
print ("##############################################################")
print ("Stats for " + target + " - " + method)
print ("Please use relevant information from below table in java side:")
train_stats = data_set[targetColumns].describe()
train_stats = train_stats.transpose()
print(train_stats)
print ("##############################################################")
#print("balancing " + target + " for " + method)
#BALANCE DATA SET
if method == "classifier":
df0 = data_set[data_set['Result']=="fail"]
df1 = data_set[data_set['Result']=="success"]
#size = min(len(df0[df0['VehicleCount']==max_vehicle]), len(df1[df1['VehicleCount']==min_vehicle]))
size = len(df0[df0['VehicleCount']==max_vehicle]) // 2
df1 = df1.groupby('VehicleCount').apply(lambda x: x if len(x) < size else x.sample(size))
df0 = df0.groupby('VehicleCount').apply(lambda x: x if len(x) < size else x.sample(size))
data_set = pd.concat([df0, df1], ignore_index=True)
else:
data_set = data_set[data_set['Result'] == 'success']
#size = min(len(data_set[data_set['VehicleCount']==min_vehicle]), len(data_set[data_set['VehicleCount']==max_vehicle]))
size = len(data_set[data_set['VehicleCount']==max_vehicle]) // 3
data_set = data_set.groupby('VehicleCount').apply(lambda x: x if len(x.index) < size else x.sample(size))
#EXTRACT RELATED ATTRIBUTES
df = pd.DataFrame(columns=targetColumns)
for column in targetColumns:
if column == 'Result' or column == 'ServiceTime':
df[column] = data_set[column]
else:
df[column] = znorm(data_set[column])
f = open(sim_result_folder + "/" + target + "_" + method + "_" + datatype + ".arff", 'w')
f.write('@relation ' + target + '\n\n')
for column in targetColumns:
if column == 'Result':
f.write('@attribute class {fail,success}\n')
else:
f.write('@attribute ' + column + ' REAL\n')
f.write('\n@data\n')
df.to_csv(f, header=False, index=False)
f.close()
print ("##############################################################")
print ("Operation completed!")
print (".arff file is generated for weka.")
print ("##############################################################")

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#!/bin/sh
python data_convertor.py config.json edge classifier train
python data_convertor.py config.json edge classifier test
python data_convertor.py config.json edge regression train
python data_convertor.py config.json edge regression test
python data_convertor.py config.json cloud_rsu classifier train
python data_convertor.py config.json cloud_rsu classifier test
python data_convertor.py config.json cloud_rsu regression train
python data_convertor.py config.json cloud_rsu regression test
python data_convertor.py config.json cloud_gsm classifier train
python data_convertor.py config.json cloud_gsm classifier test
python data_convertor.py config.json cloud_gsm regression train
python data_convertor.py config.json cloud_gsm regression test

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#!/bin/sh
javac -classpath "./json-simple-1.1.1.jar:../../../lib/weka.jar:../../../lib/mtj-1.0.4.jar" WekaModelCreator.java
java -classpath ".:./json-simple-1.1.1.jar:../../../lib/weka.jar:../../../lib/mtj-1.0.4.jar" WekaModelCreator config.json

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#!/bin/sh
rm -rf ../../bin
mkdir ../../bin
javac -classpath "../../lib/cloudsim-4.0.jar:../../lib/commons-math3-3.6.1.jar:../../lib/colt.jar:../../lib/weka.jar:../../lib/mtj-1.0.4.jar" -sourcepath ../../src ../../src/edu/boun/edgecloudsim/applications/vec_ai_app/VehicularMainApp.java -d ../../bin

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<?xml version="1.0"?>
<applications>
<application name="TRAFFIC_MANAGEMENT">
<usage_percentage>30</usage_percentage>
<prob_cloud_selection>0</prob_cloud_selection>
<delay_sensitivity>0.5</delay_sensitivity>
<max_delay_requirement>0.5</max_delay_requirement>
<poisson_interarrival>3</poisson_interarrival>
<active_period>3600</active_period>
<idle_period>1</idle_period>
<data_upload>20</data_upload>
<data_download>20</data_download>
<task_length>3000</task_length>
<required_core>1</required_core>
<vm_utilization_on_edge>6</vm_utilization_on_edge>
<vm_utilization_on_cloud>1.2</vm_utilization_on_cloud>
<vm_utilization_on_mobile>0</vm_utilization_on_mobile>
</application>
<application name="DANGER_ASSESSMENT">
<usage_percentage>35</usage_percentage>
<prob_cloud_selection>0</prob_cloud_selection>
<delay_sensitivity>0.8</delay_sensitivity>
<max_delay_requirement>1</max_delay_requirement>
<poisson_interarrival>5</poisson_interarrival>
<active_period>3600</active_period>
<idle_period>1</idle_period>
<data_upload>40</data_upload>
<data_download>20</data_download>
<task_length>10000</task_length>
<required_core>1</required_core>
<vm_utilization_on_edge>20</vm_utilization_on_edge>
<vm_utilization_on_cloud>4</vm_utilization_on_cloud>
<vm_utilization_on_mobile>0</vm_utilization_on_mobile>
</application>
<application name="INFOTAINMENT">
<usage_percentage>35</usage_percentage>
<prob_cloud_selection>0</prob_cloud_selection>
<delay_sensitivity>0.25</delay_sensitivity>
<max_delay_requirement>1.5</max_delay_requirement>
<poisson_interarrival>15</poisson_interarrival>
<active_period>3600</active_period>
<idle_period>1</idle_period>
<data_upload>20</data_upload>
<data_download>80</data_download>
<task_length>20000</task_length>
<required_core>1</required_core>
<vm_utilization_on_edge>40</vm_utilization_on_edge>
<vm_utilization_on_cloud>8</vm_utilization_on_cloud>
<vm_utilization_on_mobile>0</vm_utilization_on_mobile>
</application>
</applications>

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#default config file
simulation_time=60
warm_up_period=3
file_log_enabled=true
deep_file_log_enabled=false
#logging is disabled if it is 0
vm_load_check_interval=0.025
#logging is disabled if it is 0
location_check_interval=0.005
#logging is disabled if it is 0
ap_delay_check_interval=0.1
min_number_of_mobile_devices=100
max_number_of_mobile_devices=1800
mobile_device_counter_size=100
wan_propogation_delay=0.15
gsm_propogation_delay=0.16
lan_internal_delay=0.01
wlan_bandwidth=10
man_bandwidth=1000
wan_bandwidth=50
gsm_bandwidth=20
#all the host on cloud runs on a single datacenter
number_of_host_on_cloud_datacenter=1
number_of_vm_on_cloud_host=20
core_for_cloud_vm=2
mips_for_cloud_vm=75000
ram_for_cloud_vm=8000
storage_for_cloud_vm=125000
#each mobile device has one host which serves one VM
#all the host runs on a single datacenter due to the out of memory (oom) issue
core_for_mobile_vm=1
mips_for_mobile_vm=1000
ram_for_mobile_vm=1800
storage_for_mobile_vm=32000
#use ',' for multiple values
#orchestrator_policies=AI_TRAINER,RANDOM,PREDICTIVE,MAB,AI_BASED
orchestrator_policies=RANDOM,PREDICTIVE,GAME_THEORY,MAB,AI_BASED
#use ',' for multiple values
simulation_scenarios=ITS_SCENARIO
#mean waiting time in seconds
attractiveness_L1_mean_waiting_time=480
attractiveness_L2_mean_waiting_time=240
attractiveness_L3_mean_waiting_time=120

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%--------------------------------------------------------------
%description
% returns a value according to the given argumentssss
%--------------------------------------------------------------
function [ret_val] = getConfiguration(argType)
if(argType == 1)
ret_val = 'D:\sim_results';
elseif(argType == 2)
ret_val = 60; %simulation time (in minutes)
elseif(argType == 3)
ret_val = 2; %Number of iterations
elseif(argType == 4)
ret_val = 2; %x tick interval for number of mobile devices
elseif(argType == 5)
ret_val = {'AI_BASED','MAB','GAME_THEORY','PREDICTIVE','RANDOM'};
elseif(argType == 6)
ret_val = {'ML-based','MAB-based','Game-based','SMA-based','random'};
elseif(argType == 7)
ret_val=[6 3 11 11]; %position of figure
%ret_val=[6 3 9 9]; %position of figure
elseif(argType == 8)
ret_val = [13 12 12]; %font fize for x/y label, lengend and x/y axis
elseif(argType == 9)
ret_val = 'Number of Vehicles'; %Common text for x axis
elseif(argType == 10)
ret_val = 100; %min number of mobile device
elseif(argType == 11)
ret_val = 100; %step size of mobile device count
elseif(argType == 12)
ret_val = 1800; %max number of mobile device
elseif(argType == 17)
ret_val = 0; %return 1 if you want to add 10^n text at x axis
elseif(argType == 18)
ret_val = 1; %return 1 if you want to save figure as pdf
elseif(argType == 19)
ret_val = 1; %return 1 if you want to plot errors
elseif(argType == 20)
ret_val=0; %return 1 if graph is plotted colerful
elseif(argType == 21)
ret_val=[0.55 0 0]; %color of first line
elseif(argType == 22)
ret_val=[0 0.15 0.6]; %color of second line
elseif(argType == 23)
ret_val=[0 0.23 0]; %color of third line
elseif(argType == 24)
ret_val=[0.6 0 0.6]; %color of fourth line
elseif(argType == 25)
ret_val=[0.08 0.08 0.08]; %color of fifth line
elseif(argType == 26)
ret_val=[0 0.8 0.8]; %color of sixth line
elseif(argType == 27)
ret_val=[0.8 0.4 0]; %color of seventh line
elseif(argType == 28)
ret_val=[0.8 0.8 0]; %color of eighth line
elseif(argType == 40)
ret_val={'-k*','-ko','-ks','-kv','-kp','-kd','-kx','-kh'}; %line style (marker) of the colerless line
elseif(argType == 50)
ret_val={'-k*','-ko','-ks','-kv','-kp','-kd','-kx','-kh'}; %line style (marker) of the colerfull line
end
end

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function [] = plotApDelay()
folderPath = getConfiguration(1);
numOfSimulations = getConfiguration(3);
stepOfxAxis = getConfiguration(4);
startOfMobileDeviceLoop = getConfiguration(10);
stepOfMobileDeviceLoop = getConfiguration(11);
endOfMobileDeviceLoop = getConfiguration(12);
numOfMobileDevices = (endOfMobileDeviceLoop - startOfMobileDeviceLoop)/stepOfMobileDeviceLoop + 1;
placeTypes = {'AP 1 (60 km/h)','Ap 4 (40 km/h)','AP 11 (20 km/h)'};
results = zeros(size(placeTypes,2),numOfMobileDevices);
for s=1:numOfSimulations
indexCounter = 1;
for i=startOfMobileDeviceLoop:stepOfMobileDeviceLoop:endOfMobileDeviceLoop
try
filePath1 = strcat(folderPath,'\ite',int2str(s),'\SIMRESULT_ITS_SCENARIO_AI_BASED_',int2str(i),'DEVICES_AP_UPLOAD_DELAY.log');
filePath2 = strcat(folderPath,'\ite',int2str(s),'\SIMRESULT_ITS_SCENARIO_AI_BASED_',int2str(i),'DEVICES_AP_DOWNLOAD_DELAY.log');
readData1 = dlmread(filePath1,';',60,0);
readData2 = dlmread(filePath2,';',60,0);
for j=1:size(placeTypes,2)
results(j,indexCounter) = results(j,indexCounter) + mean(readData1(:,j+1)) + mean(readData2(:,j+1));
end
catch err
error(err)
end
indexCounter = indexCounter + 1;
end
end
results = results/numOfSimulations;
types = zeros(1,numOfMobileDevices);
for i=1:numOfMobileDevices
types(i)=startOfMobileDeviceLoop+((i-1)*stepOfMobileDeviceLoop);
end
hFig = figure;
pos=getConfiguration(7);
fontSizeArray = getConfiguration(8);
set(hFig, 'Units','centimeters');
set(hFig, 'Position',pos);
set(0,'DefaultAxesFontName','Times New Roman');
set(0,'DefaultTextFontName','Times New Roman');
set(0,'DefaultAxesFontSize',fontSizeArray(3));
if(getConfiguration(20) == 1)
for i=stepOfxAxis:stepOfxAxis:numOfMobileDevices
xIndex=startOfMobileDeviceLoop+((i-1)*stepOfMobileDeviceLoop);
markers = {':k*',':ko',':ks',':kv'};
for j=1:size(placeTypes,2)
plot(xIndex, results(j,i),char(markers(j)),'MarkerFaceColor',getConfiguration(20+j),'color',getConfiguration(20+j));
hold on;
end
end
for j=1:size(placeTypes,2)
plot(types, results(j,:),':k','color',getConfiguration(20+j),'LineWidth',1.5);
hold on;
end
set(gca,'color','none');
else
markers = {'-k*','-ko','-ks','-kv'};
for j=1:size(placeTypes,2)
plot(types, results(j,:),char(markers(j)),'MarkerFaceColor','w','LineWidth',1.4);
hold on;
end
%set(gcf, 'Position',getConfiguration(28));
end
lgnd = legend(placeTypes,'Location','NorthWest');
if(getConfiguration(20) == 1)
set(lgnd,'color','none');
end
if(getConfiguration(20) == 1)
set(lgnd,'color','none');
end
if(getConfiguration(17) == 0)
manualXAxisCoefficent = 1;
end
hold off;
axis square
xlabel(getConfiguration(9));
set(gca,'XTick', startOfMobileDeviceLoop + stepOfMobileDeviceLoop:(stepOfxAxis*stepOfMobileDeviceLoop):endOfMobileDeviceLoop);
set(gca,'XTickLabel', startOfMobileDeviceLoop + stepOfMobileDeviceLoop/manualXAxisCoefficent:(stepOfxAxis*stepOfMobileDeviceLoop)/manualXAxisCoefficent:endOfMobileDeviceLoop/manualXAxisCoefficent);
ylabel('Average Network Delay (sec)');
set(gca,'XLim',[startOfMobileDeviceLoop-5 endOfMobileDeviceLoop+5]);
if(getConfiguration(17) == 1)
xlim = get(gca,'XLim');
ylim = get(gca,'YLim');
text(1.02 * xlim(2), 0.165 * ylim(2), 'x 10^2');
end
set(get(gca,'Xlabel'),'FontSize',fontSizeArray(1));
set(get(gca,'Ylabel'),'FontSize',fontSizeArray(1));
set(lgnd,'FontSize',fontSizeArray(2));
if(getConfiguration(18) == 1)
set(hFig, 'PaperUnits', 'centimeters');
set(hFig, 'PaperPositionMode', 'manual');
set(hFig, 'PaperPosition',[0 0 pos(3) pos(4)]);
set(gcf, 'PaperSize', [pos(3) pos(4)]); %Keep the same paper size
filename = strcat(folderPath,'\apDelay');
saveas(gcf, filename, 'pdf');
end
end

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function [] = plotAvgFailedTask()
plotGenericLine(1, 2, 'Average Failed Tasks (%)', 'ALL_APPS', 'NorthWest', 1);
plotGenericLine(1, 2, 'Failed Tasks for Danger Assessment App (%)', 'DANGER_ASSESSMENT', 'NorthWest', 1);
plotGenericLine(1, 2, 'Failed Tasks for Navigation App (%)', 'TRAFFIC_MANAGEMENT', 'NorthWest', 1);
plotGenericLine(1, 2, 'Failed Tasks for Infotainment App (%)', 'INFOTAINMENT', 'NorthWest', 1);
end

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scripts/sample_app5/matlab/plotAvgNetworkDelay.m Normal file
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function [] = plotAvgNetworkDelay()
plotGenericLine(1, 7, 'Average Network Delay (sec)', 'ALL_APPS', 'NorthWest', 0);
plotGenericLine(5, 1, 'Average WLAN Delay (sec)', 'ALL_APPS', 'NorthWest', 0);
plotGenericLine(5, 2, 'Average MAN Delay (sec)', 'ALL_APPS', 'NorthWest', 0);
plotGenericLine(5, 3, 'Average WAN Delay (sec)', 'ALL_APPS', 'NorthWest', 0, 1, 1);
plotGenericLine(5, 4, 'Average GSM Delay (sec)', 'ALL_APPS', 'NorthWest', 0, 1, 1, 0, [4, 1700]);
end

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scripts/sample_app5/matlab/plotAvgProcessingTime.m Normal file
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function [] = plotAvgProcessingTime()
plotGenericLine(1, 6, 'Average Processing Time (sec)', 'ALL_APPS', 'NorthWest', 0);
plotGenericLine(2, 6, 'Processing Time on RSU (sec)', 'ALL_APPS', 'NorthWest', 0);
plotGenericLine(3, 6, 'Processing Time on Cloud (sec)', 'ALL_APPS', 'NorthWest', 0);
end

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scripts/sample_app5/matlab/plotAvgQoE.m Normal file
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function [] = plotAvgQoE()
plotGenericLine(1, 13, 'Average QoE (%)', 'ALL_APPS', 'SouthWest', 0);
plotGenericLine(1, 13, 'QoE for Danger Assessment App (%)', 'DANGER_ASSESSMENT', 'SouthWest', 0);
plotGenericLine(1, 13, 'QoE for Navigation App (%)', 'TRAFFIC_MANAGEMENT', 'SouthWest', 0);
plotGenericLine(1, 13, 'QoE for Infotainment App (%)', 'INFOTAINMENT', 'SouthWest', 0);
end

9
scripts/sample_app5/matlab/plotAvgServiceTime.m Normal file
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function [] = plotAvgServiceTime()
plotGenericLine(1, 5, 'Average Service Time (sec)', 'ALL_APPS', 'NorthWest', 0);
plotGenericLine(2, 5, 'Service Time on RSU (sec)', 'ALL_APPS', 'NorthWest', 0);
plotGenericLine(3, 5, 'Service Time on Cloud (sec)', 'ALL_APPS', 'NorthWest', 0);
end

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scripts/sample_app5/matlab/plotAvgVmUtilization.m Normal file
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function [] = plotAvgVmUtilization()
plotGenericLine(2, 8, 'Average VM Utilization of RSU (%)', 'ALL_APPS', 'NorthWest', 0);
plotGenericLine(3, 8, 'Average VM Utilization of Cloud (%)', 'ALL_APPS', 'NorthWest', 0);
end

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function [] = plotDelayReasonAsBar(isEdge)
folderPath = getConfiguration(1);
numOfSimulations = getConfiguration(3);
stepOfxAxis = 3;%getConfiguration(4);
startOfMobileDeviceLoop = getConfiguration(10);
stepOfMobileDeviceLoop = getConfiguration(11);
endOfMobileDeviceLoop = getConfiguration(12);
numOfMobileDevices = (endOfMobileDeviceLoop - startOfMobileDeviceLoop)/stepOfMobileDeviceLoop + 1;
all_results = zeros(numOfSimulations, numOfMobileDevices, 2);
if ~exist('isEdge','var')
isEdge = 1;
end
for s=1:numOfSimulations
for j=1:numOfMobileDevices
try
mobileDeviceNumber = startOfMobileDeviceLoop + stepOfMobileDeviceLoop * (j-1);
filePath = strcat(folderPath,'\ite',int2str(s),'\SIMRESULT_ITS_SCENARIO_AI_BASED_',int2str(mobileDeviceNumber),'DEVICES_ALL_APPS_GENERIC.log');
readData = dlmread(filePath,';',1,0);
value1 = 0;
value2 = 0;
if(isEdge == 1)
value1 = readData(2,5);
value2 = readData(2,6);
else
value1 = readData(3,5);
value2 = readData(3,6);
end
all_results(s,j,1) = value2;
all_results(s,j,2) = value1 - value2;
catch err
error(err)
end
end
end
if(numOfSimulations == 1)
results = all_results;
else
results = mean(all_results); %still 3d matrix but 1xMxN format
end
results = squeeze(results); %remove singleton dimensions
hFig=figure;
pos=getConfiguration(7);
set(hFig, 'Units','centimeters');
set(hFig, 'Position',pos);
set(0,'DefaultAxesFontName','Times New Roman');
set(0,'DefaultTextFontName','Times New Roman');
set(0,'DefaultAxesFontSize',11);
set(0,'DefaultTextFontSize',12);
b = bar(results,'stacked');
%set(b(1),'LineStyle','--');
if(isEdge == 1)
lgnd = legend(b,{'processing time','WLAN delay'},'Location','NorthWest');
ylabel({'Service Time on Edge (sec)'});
filename = 'edge_delay_reason';
else
lgnd = legend(b,{'processing time','WAN delay'},'Location','NorthWest');
ylabel({'Service Time on Cloud(sec)'});
filename = 'cloud_delay_reason';
end
set(b,{'FaceColor'},{[.45 .45 .45];[.90 .90 .90]});
axis square
xlabel(getConfiguration(9));
set(gca,'XTick', stepOfxAxis:stepOfxAxis:numOfMobileDevices);
set(gca,'XTickLabel', (startOfMobileDeviceLoop*stepOfxAxis):(stepOfxAxis*stepOfMobileDeviceLoop):endOfMobileDeviceLoop);
set(gca,'XLim',[0 numOfMobileDevices+1])
set(get(gca,'Xlabel'),'FontSize',12)
set(get(gca,'Ylabel'),'FontSize',12)
set(lgnd,'FontSize',12)
if(getConfiguration(18) == 1)
set(hFig, 'PaperUnits', 'centimeters');
set(hFig, 'PaperPositionMode', 'manual');
set(hFig, 'PaperPosition',[0 0 pos(3) pos(4)]);
set(gcf, 'PaperSize', [pos(3) pos(4)]); %Keep the same paper size
filename = strcat(folderPath,'\',filename);
saveas(gcf, filename, 'pdf');
end
end

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function [] = plotGenericLine(rowOfset, columnOfset, yLabel, appType, legendPos, calculatePercentage, divisor, ignoreZeroValues, hideLowerValues, hideXAxis)
folderPath = getConfiguration(1);
numOfSimulations = getConfiguration(3);
stepOfxAxis = getConfiguration(4);
scenarioType = getConfiguration(5);
startOfMobileDeviceLoop = getConfiguration(10);
stepOfMobileDeviceLoop = getConfiguration(11);
endOfMobileDeviceLoop = getConfiguration(12);
numOfMobileDevices = (endOfMobileDeviceLoop - startOfMobileDeviceLoop)/stepOfMobileDeviceLoop + 1;
all_results = zeros(numOfSimulations, size(scenarioType,2), numOfMobileDevices);
min_results = zeros(size(scenarioType,2), numOfMobileDevices);
max_results = zeros(size(scenarioType,2), numOfMobileDevices);
if ~exist('appType','var')
appType = 'ALL_APPS';
end
if ~exist('divisor','var')
divisor = 1;
end
if ~exist('ignoreZeroValues','var')
ignoreZeroValues = 0;
end
if ~exist('hideLowerValues','var')
hideLowerValues = 0;
end
if exist('hideXAxis','var')
hideXAxisStartValue = hideXAxis(2);
hideXAxisIndex = hideXAxis(1);
end
for s=1:numOfSimulations
for i=1:size(scenarioType,2)
for j=1:numOfMobileDevices
try
mobileDeviceNumber = startOfMobileDeviceLoop + stepOfMobileDeviceLoop * (j-1);
filePath = strcat(folderPath,'\ite',int2str(s),'\SIMRESULT_ITS_SCENARIO_',char(scenarioType(i)),'_',int2str(mobileDeviceNumber),'DEVICES_',appType,'_GENERIC.log');
readData = dlmread(filePath,';',rowOfset,0);
value = readData(1,columnOfset);
if(calculatePercentage==1)
readData = dlmread(filePath,';',1,0);
totalTask = readData(1,1)+readData(1,2);
value = (100 * value) / totalTask;
end
all_results(s,i,j) = value;
catch err
error(err)
end
end
end
end
if(numOfSimulations == 1)
results = all_results;
else
if(ignoreZeroValues == 1)
results = sum(all_results,1) ./ sum(all_results~=0,1);
%TODO cahnge NaN to 0
else
results = mean(all_results); %still 3d matrix but 1xMxN format
end
end
results = squeeze(results); %remove singleton dimensions
for i=1:size(scenarioType,2)
for j=1:numOfMobileDevices
if(results(i,j) < hideLowerValues)
results(i,j) = NaN;
else
results(i,j) = results(i,j) / divisor;
end
end
end
if exist('hideXAxis','var')
for j=1:numOfMobileDevices
if(j*stepOfMobileDeviceLoop+startOfMobileDeviceLoop > hideXAxisStartValue)
results(hideXAxisIndex,j) = NaN;
end
end
end
for i=1:size(scenarioType,2)
for j=1:numOfMobileDevices
x=results(i,j); % Create Data
SEM = std(x)/sqrt(length(x)); % Standard Error
ts = tinv([0.05 0.95],length(x)-1); % T-Score
CI = mean(x) + ts*SEM; % Confidence Intervals
if(CI(1) < 0)
CI(1) = 0;
end
if(CI(2) < 0)
CI(2) = 0;
end
min_results(i,j) = results(i,j) - CI(1);
max_results(i,j) = CI(2) - results(i,j);
end
end
types = zeros(1,numOfMobileDevices);
for i=1:numOfMobileDevices
types(i)=startOfMobileDeviceLoop+((i-1)*stepOfMobileDeviceLoop);
end
hFig = figure;
pos=getConfiguration(7);
fontSizeArray = getConfiguration(8);
set(hFig, 'Units','centimeters');
set(hFig, 'Position',pos);
set(0,'DefaultAxesFontName','Times New Roman');
set(0,'DefaultTextFontName','Times New Roman');
set(0,'DefaultAxesFontSize',fontSizeArray(3));
if(getConfiguration(20) == 1)
for i=1:1:numOfMobileDevices
xIndex=startOfMobileDeviceLoop+((i-1)*stepOfMobileDeviceLoop);
markers = getConfiguration(50);
for j=1:size(scenarioType,2)
plot(xIndex, results(j,i),char(markers(j)),'MarkerFaceColor',getConfiguration(20+j),'color',getConfiguration(20+j));
hold on;
end
end
for j=1:size(scenarioType,2)
if(getConfiguration(19) == 1)
errorbar(types, results(j,:), min_results(j,:),max_results(j,:),'-k','color',getConfiguration(20+j),'LineWidth',1);
else
plot(types, results(j,:),'-k','color',getConfiguration(20+j),'LineWidth',1);
end
hold on;
end
set(gca,'color','none');
else
markers = getConfiguration(40);
for j=1:size(scenarioType,2)
if(getConfiguration(19) == 1)
errorbar(types, results(j,:),min_results(j,:),max_results(j,:),char(markers(j)),'MarkerFaceColor','w','LineWidth',1);
else
plot(types, results(j,:),char(markers(j)),'MarkerFaceColor','w');
end
hold on;
end
end
legends = getConfiguration(6);
lgnd = legend(legends,'Location',legendPos);
%lgnd.Position=[0.21,0.8,0.2,0.01];
if(getConfiguration(20) == 1)
set(lgnd,'color','none');
end
xCoefficent = 100;
if(getConfiguration(17) == 0)
xCoefficent = 1;
end
hold off;
axis square
xlabel(getConfiguration(9));
step = stepOfxAxis*stepOfMobileDeviceLoop;
set(gca,'XTick', step:step:endOfMobileDeviceLoop);
set(gca,'XTickLabel', step/xCoefficent:step/xCoefficent:endOfMobileDeviceLoop/xCoefficent);
ylabel(yLabel);
set(gca,'XLim',[startOfMobileDeviceLoop-5 endOfMobileDeviceLoop+5]);
if(getConfiguration(17) == 1)
xlim = get(gca,'XLim');
ylim = get(gca,'YLim');
text(1.02 * xlim(2), 0.165 * ylim(2), 'x 10^2');
end
set(get(gca,'Xlabel'),'FontSize',fontSizeArray(1));
set(get(gca,'Ylabel'),'FontSize',fontSizeArray(1));
set(lgnd,'FontSize',fontSizeArray(2));
if(getConfiguration(18) == 1)
set(hFig, 'PaperUnits', 'centimeters');
set(hFig, 'PaperPositionMode', 'manual');
set(hFig, 'PaperPosition',[0 0 pos(3) pos(4)]);
set(gcf, 'PaperSize', [pos(3) pos(4)]); %Keep the same paper size
filename = strcat(folderPath,'\',int2str(rowOfset),'_',int2str(columnOfset),'_',appType);
saveas(gcf, filename, 'pdf');
end
end

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scripts/sample_app5/matlab/plotGenericPie.m Normal file
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function [] = plotGenericPie(vmType, appType, threshold)
folderPath = "D:\git-repos\PhD\EdgeCloudSim\sim_results";
scenarioType = getConfiguration(5);
startOfMobileDeviceLoop = 2000;
stepOfMobileDeviceLoop = 2000;
endOfMobileDeviceLoop = 2000;
numOfMobileDevices = (endOfMobileDeviceLoop - startOfMobileDeviceLoop)/stepOfMobileDeviceLoop + 1;
if ~exist('appType','var')
appType = 1;
end
if ~exist('vmType','var')
vmType = 'edge';
end
total = zeros(1,size(scenarioType,2));
found = zeros(1,size(scenarioType,2));
for s=1:size(scenarioType,2)
for j=1:numOfMobileDevices
try
mobileDeviceNumber = startOfMobileDeviceLoop + stepOfMobileDeviceLoop * (j-1);
filePath = strcat(folderPath,'\ite11\SIMRESULT_ITS_SCENARIO_',char(scenarioType(s)),'_',int2str(mobileDeviceNumber),'DEVICES_SUCCESS.log');
readData = dlmread(filePath,';',1,0);
for k=1:size(readData,1)
if(readData(k,7) == appType && ((strcmp(vmType,'edge') == 1 && readData(k,3) == 3) || (strcmp(vmType,'cloud') == 1 && readData(k,3) ~= 3) || strcmp(vmType,'all')==1))
if(readData(k,12) - readData(k,11) > threshold)
found(s) = found(s) + 1;
end
total(s) = total(s) + 1;
end
end
catch err
error(err)
end
end
end
hFig = figure;
pos=getConfiguration(7);
fontSizeArray = getConfiguration(8);
set(hFig, 'Units','centimeters');
set(hFig, 'Position',pos);
set(0,'DefaultAxesFontName','Times New Roman');
set(0,'DefaultTextFontName','Times New Roman');
set(0,'DefaultAxesFontSize',fontSizeArray(3));
if(getConfiguration(20) == 1)
%result = found ./ total .* 100;
p = pie(found);
hold on;
end
txt = reshape(getConfiguration(6),[size(scenarioType,2) 1]);
pText = findobj(p,'Type','text');
percentValues = get(pText,'String');
combinedtxt = strcat(txt,' (',percentValues,')');
for i=1:size(scenarioType,2)
pText(i).String = combinedtxt(i);
end
set(pText,'fontsize',fontSizeArray(1))
hold off;
axis square
if(getConfiguration(18) == 1)
set(hFig, 'PaperUnits', 'centimeters');
set(hFig, 'PaperPositionMode', 'manual');
set(hFig, 'PaperPosition',[0 0 pos(3) pos(4)]);
set(gcf, 'PaperSize', [pos(3) pos(4)]); %Keep the same paper size
filename = strcat(folderPath,'\',int2str(rowOfset),'_',int2str(columnOfset),'_',appType);
saveas(gcf, filename, 'pdf');
end
end

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scripts/sample_app5/matlab/plotGenericScatter.m Normal file
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function [] = plotGenericScatter(yLabel, xLabel, legendPos, vmType, appType, drawLine)
folderPath = "D:\git-repos\PhD\EdgeCloudSim\sim_results";
scenarioType = getConfiguration(5);
simulationTime = getConfiguration(2);
startOfMobileDeviceLoop = 2000;
stepOfMobileDeviceLoop = 2000;
endOfMobileDeviceLoop = 2000;
numOfMobileDevices = (endOfMobileDeviceLoop - startOfMobileDeviceLoop)/stepOfMobileDeviceLoop + 1;
if ~exist('appType','var')
appType = 1;
end
if ~exist('vmType','var')
vmType = 'edge';
end
result_x = NaN(size(scenarioType,2), 5000);
result_y = NaN(size(result_x,1), size(result_x,2));
%result_sz = NaN(size(result_x,1), size(result_x,2));
for s=1:size(scenarioType,2)
index = 1;
firstDeviceId = -1;
for j=1:numOfMobileDevices
try
mobileDeviceNumber = startOfMobileDeviceLoop + stepOfMobileDeviceLoop * (j-1);
filePath = strcat(folderPath,'\ite11\SIMRESULT_ITS_SCENARIO_',char(scenarioType(s)),'_',int2str(mobileDeviceNumber),'DEVICES_SUCCESS.log');
readData = dlmread(filePath,';',1,0);
for k=1:size(readData,1)
if(readData(k,7) == appType && ((strcmp(vmType,'edge') == 1 && readData(k,3) == 3) || (strcmp(vmType,'cloud') == 1 && readData(k,3) ~= 3) || strcmp(vmType,'all')==1))
if(firstDeviceId == -1)
firstDeviceId = readData(k,2);
end
if(readData(k,2) == firstDeviceId) % && readData(k,11) - readData(k,10) < 2
result_y(s, index) = readData(k,12) - readData(k,11);
result_x(s, index) = readData(k,12) / 60;
%result_sz(s, index) = j*10;
index = index + 1;
end
end
end
catch err
error(err)
end
end
end
hFig = figure;
pos=getConfiguration(7);
fontSizeArray = getConfiguration(8);
set(hFig, 'Units','centimeters');
set(hFig, 'Position',pos);
set(0,'DefaultAxesFontName','Times New Roman');
set(0,'DefaultTextFontName','Times New Roman');
set(0,'DefaultAxesFontSize',fontSizeArray(3));
if(getConfiguration(20) == 1)
for i=1:size(scenarioType,2)
scatter(result_x(i,:), result_y(i,:));
hold on;
end
if exist('drawLine','var')
y = [drawLine, drawLine];
x = [0, simulationTime];
plot(x,y);
end
end
legends = getConfiguration(6);
lgnd = legend(legends,'Location',legendPos);
%lgnd.Position=[0.21,0.8,0.2,0.01];
if(getConfiguration(20) == 1)
set(lgnd,'color','none');
end
hold off;
axis square
xlabel(xLabel);
ylabel(yLabel);
set(get(gca,'Xlabel'),'FontSize',fontSizeArray(1));
set(get(gca,'Ylabel'),'FontSize',fontSizeArray(1));
set(lgnd,'FontSize',fontSizeArray(2));
if(getConfiguration(18) == 1)
set(hFig, 'PaperUnits', 'centimeters');
set(hFig, 'PaperPositionMode', 'manual');
set(hFig, 'PaperPosition',[0 0 pos(3) pos(4)]);
set(gcf, 'PaperSize', [pos(3) pos(4)]); %Keep the same paper size
filename = strcat(folderPath,'\',int2str(rowOfset),'_',int2str(columnOfset),'_',appType);
saveas(gcf, filename, 'pdf');
end
end

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scripts/sample_app5/matlab/plotLocation.m Normal file
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function [] = plotLocation()
folderPath = getConfiguration(1);
numOfSimulations = getConfiguration(3);
stepOfxAxis = getConfiguration(4);
startOfMobileDeviceLoop = 500;
stepOfMobileDeviceLoop = 500;
endOfMobileDeviceLoop = 1500;
numOfMobileDevices = (endOfMobileDeviceLoop - startOfMobileDeviceLoop)/stepOfMobileDeviceLoop + 1;
PlaceCount = 40;
results = zeros(numOfMobileDevices, PlaceCount);
for s=1:numOfSimulations
indexCounter = 1;
for i=startOfMobileDeviceLoop:stepOfMobileDeviceLoop:endOfMobileDeviceLoop
try
filePath = strcat(folderPath,'\ite',int2str(s),'\SIMRESULT_ITS_SCENARIO_AI_BASED_',int2str(i),'DEVICES_LOCATION.log');
readData = dlmread(filePath,';',1,0);
for j=1:PlaceCount
results(indexCounter,j) = results(indexCounter,j) + mean(readData(:,j+1));
end
catch err
error(err)
end
indexCounter = indexCounter + 1;
end
end
results = results/numOfSimulations;
xValues = {};
yValues = {};
indexCounter = 1;
for i=startOfMobileDeviceLoop:stepOfMobileDeviceLoop:endOfMobileDeviceLoop
xValues(indexCounter) = {int2str(i)};
indexCounter = indexCounter + 1;
end
for i=1:PlaceCount
yValues(i) = {int2str(i)}
end
hFig=figure;
%pos=getConfiguration(7);
pos=[10 3 24 6];
set(hFig, 'Units','centimeters');
set(hFig, 'Position',pos);
C = [0,172,0;2,171,0;5,171,0;7,171,0;10,171,0;12,171,0;15,171,0;17,171,0;20,171,0;22,171,0;25,170,0;27,170,0;30,170,0;32,170,0;35,170,0;37,170,0;40,170,0;42,170,0;45,170,0;47,169,0;50,169,0;52,169,0;55,169,0;57,169,0;60,169,0;62,169,0;65,169,0;67,169,0;70,168,0;72,168,0;75,168,0;77,168,0;80,168,0;82,168,0;85,168,0;87,168,0;90,168,0;92,168,0;95,167,0;97,167,0;100,167,0;102,167,0;105,167,0;107,167,0;110,167,0;112,167,0;115,167,0;117,166,0;120,166,0;122,166,0;125,166,0;127,166,0;130,166,0;132,166,0;135,166,0;137,166,0;140,165,0;142,165,0;145,165,0;147,165,0;150,165,0;152,165,0;155,165,0;157,165,0;159,164,0;160,164,0;161,163,0;162,162,0;163,162,0;163,161,0;164,160,0;165,159,0;166,159,0;167,158,0;168,157,0;168,157,0;169,156,0;170,155,0;171,154,0;172,154,0;172,153,0;173,152,0;174,152,0;175,151,0;176,150,0;176,149,0;177,149,0;178,148,0;179,147,0;180,147,0;181,146,0;181,145,0;182,144,0;183,144,0;184,143,0;185,142,0;185,142,0;186,141,0;187,140,0;188,139,0;189,139,0;189,138,0;190,137,0;191,137,0;192,136,0;193,135,0;193,134,0;194,134,0;195,133,0;196,132,0;197,132,0;198,131,0;198,130,0;199,129,0;200,129,0;201,128,0;202,127,0;202,127,0;203,126,0;204,125,0;205,124,0;206,124,0;206,123,0;207,122,0;208,122,0;209,121,0;210,120,0;211,119,0;211,119,0;211,118,0;211,117,0;210,116,0;210,115,0;210,114,0;210,113,0;210,112,0;210,111,0;209,110,0;209,109,0;209,108,0;209,107,0;209,106,0;208,105,0;208,104,0;208,103,0;208,102,0;208,102,0;208,101,0;207,100,0;207,99,0;207,98,0;207,97,0;207,96,0;207,95,0;206,94,0;206,93,0;206,92,0;206,91,0;206,90,0;206,89,0;205,88,0;205,87,0;205,86,0;205,85,0;205,84,0;205,84,0;204,83,0;204,82,0;204,81,0;204,80,0;204,79,0;204,78,0;203,77,0;203,76,0;203,75,0;203,74,0;203,73,0;203,72,0;202,71,0;202,70,0;202,69,0;202,68,0;202,67,0;202,67,0;201,66,0;201,65,0;201,64,0;201,63,0;201,62,0;201,61,0;200,60,0;200,59,0;199,58,0;198,57,0;197,56,0;196,55,0;195,54,0;194,53,0;193,53,0;192,52,0;191,51,0;190,50,0;189,49,0;188,48,0;187,47,0;186,46,0;185,45,0;184,44,0;183,43,0;182,42,0;181,41,0;180,41,0;179,40,0;177,39,0;176,38,0;175,37,0;174,36,0;173,35,0;172,34,0;171,33,0;170,32,0;169,31,0;168,30,0;167,29,0;166,29,0;165,28,0;164,27,0;163,26,0;162,25,0;161,24,0;160,23,0;159,22,0;158,21,0;157,20,0;156,19,0;155,18,0;153,18,0;152,17,0;151,16,0;150,15,0;149,14,0;148,13,0;147,12,0;146,11,0;145,10,0;144,9,0;143,8,0;142,7,0;141,6,0;140,6,0;139,5,0;138,4,0;137,3,0;136,2,0;135,1,0;134,0,0];
h = heatmap(yValues, xValues, results, 'Colormap', colormap(C/255),'FontName','Times New Roman','FontSize',11);
h.Title = 'Mean number of vehicles per places';
h.XLabel = 'Place IDs';
h.YLabel = '#Vehicle in simulation';
if(getConfiguration(18) == 1)
set(hFig, 'PaperUnits', 'centimeters');
set(hFig, 'PaperPositionMode', 'manual');
set(hFig, 'PaperPosition',[0 0 pos(3) pos(4)]);
set(gcf, 'PaperSize', [pos(3) pos(4)]); %Keep the same paper size
filename = strcat(folderPath,'\position');
saveas(gcf, filename, 'pdf');
end
end

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scripts/sample_app5/matlab/plotTaskFailureReason.m Normal file
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function [] = plotTaskFailureReason()
plotGenericLine(1, 10, 'Failed Task due to VM Capacity (%)', 'ALL_APPS', 'NorthWest', 1);
plotGenericLine(1, 11, 'Failed Task due to Mobility (%)', 'ALL_APPS', 'NorthWest', 1);
plotGenericLine(5, 5, 'Failed Tasks due to WLAN (%)', 'ALL_APPS', 'NorthWest', 1);
plotGenericLine(5, 6, 'Failed Tasks due to MAN failure (%)', 'ALL_APPS', 'NorthWest', 1);
plotGenericLine(5, 7, 'Failed Tasks due to WAN failure (%)', 'ALL_APPS', 'NorthWest', 1);
plotGenericLine(5, 8, 'Failed Tasks due to GSM failure (%)', 'ALL_APPS', 'NorthEast', 1);
end

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scripts/sample_app5/matlab/plotTimeComplexity.m Normal file
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function [] = plotTimeComplexity()
plotGenericLine(6, 1, 'Simulation Time (minute)', 'ALL_APPS', 'NorthWest', 0, 60);
plotGenericLine(6, 2, 'Orchestration Algorithm Overhead (micro second)', 'ALL_APPS', 'NorthEast', 0, 1000);
end

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#!/bin/bash
if [ "$#" -ne 2 ]; then
echo "Missing arguments! Please provide number of parallel processes and number of iterations."
echo "Usage: '$0 4 10'"
exit 1
fi
re='^[0-9]+$'
if ! [[ $1 =~ $re ]] ; then
echo "$1 is not an integer! Please provide number of parallel processes."
echo "Usage: '$0 4 10'"
exit 1
fi
if ! [[ $2 =~ $re ]] ; then
echo "$1 is not an integer! Please provide number of iterations."
echo "Usage: '$0 4 10'"
exit 1
fi
script_root_path="$(dirname "$(readlink -f "$0")")"
root_out_folder=${script_root_path}/output
num_of_processes=$1
iterationNumber=$2
process_counter=0
date=$(date '+%d-%m-%Y_%H-%M')
simulation_out_folder=${root_out_folder}/${date}
mkdir -p $simulation_out_folder
simulations=$(cat ${script_root_path}/simulation.list)
rm -rf ${script_root_path}/tmp_runner*
for sim_args in $simulations
do
scenario_name=$(echo $sim_args | cut -d ';' -f1)
edge_devices_file=$(echo $sim_args | cut -d ';' -f2)
applications_file=$(echo $sim_args | cut -d ';' -f3)
mkdir -p $simulation_out_folder/${scenario_name}
echo "STARTED" > $simulation_out_folder/${scenario_name}/progress.log
for (( i=1; i<=$iterationNumber; i++ ))
do
process_id=$(($process_counter % $num_of_processes))
process_counter=$(($process_counter + 1))
echo "${script_root_path}/runner.sh $simulation_out_folder $scenario_name $edge_devices_file $applications_file ${i}" >> "${simulation_out_folder}/tmp_runner${process_id}.sh"
done
done
#num_of_cores=$(grep -c ^processor /proc/cpuinfo)
for (( i=0; i<$num_of_processes; i++ ))
do
chmod +x ${simulation_out_folder}/tmp_runner${i}.sh
${simulation_out_folder}/tmp_runner${i}.sh &
# pid=$!
# cpu=$(($i % $num_of_cores))
# taskset -cp $cpu,$cpu $pid
done
echo "###############################################################"
echo " SIMULARIONS ARE STARTED!"
echo "###############################################################"
echo "You can follow the progress via the following command"
echo "tail -f <scenario_folder>/progress.log"
echo "e.g."
echo "tail -f output/${date}/${scenario_name}/progress.log"
echo "###############################################################"
echo "You can inspect each iteration via the following command"
echo "tail -f <scenario_folder>/ite[n].log"
echo "e.g."
echo "tail -f output/${date}/${scenario_name}/ite1.log"
echo "###############################################################"

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scripts/sample_app5/runner.sh Normal file
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#!/bin/sh
script_root_path="$(dirname "$(readlink -f "$0")")"
simulation_out_folder=$1
scenario_name=$2
edge_devices_file=$3
applications_file=$4
iteration_number=$5
scenario_out_folder=${simulation_out_folder}/${scenario_name}/ite${iteration_number}
scenario_conf_file=${script_root_path}/config/${scenario_name}.properties
scenario_edge_devices_file=${script_root_path}/config/${edge_devices_file}
scenario_applications_file=${script_root_path}/config/${applications_file}
mkdir -p $scenario_out_folder
java -classpath '../../bin:../../lib/cloudsim-4.0.jar:../../lib/commons-math3-3.6.1.jar:../../lib/colt.jar:../../lib/weka.jar:../../lib/mtj-1.0.4.jar' -Djava.library.path=../../lib/native/linux edu.boun.edgecloudsim.applications.vec_ai_app.VehicularMainApp $scenario_conf_file $scenario_edge_devices_file $scenario_applications_file $scenario_out_folder $iteration_number > ${scenario_out_folder}.log
if [ $? -eq 0 ]; then
echo "ite${iteration_number} OK" >> ${simulation_out_folder}/${scenario_name}/progress.log
else
echo "ite${iteration_number} FAIL !!!" >> ${simulation_out_folder}/${scenario_name}/progress.log
fi
#tar -czf ${scenario_out_folder}.tar.gz -C $simulation_out_folder/${scenario_name} ite${iteration_number}
#rm -rf $scenario_out_folder

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default_config;edge_devices.xml;applications.xml

15
scripts/sample_app5/stop_scenarios.sh Normal file
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#!/bin/sh
echo "This script kills all java processes including Eclipse etc."
read -p "Do you want to continue[y/n]? " REPLY
if [ "$REPLY" = "y" ]
then
while pgrep java >/dev/null 2>&1
do
killall java
sleep 1
done
fi
echo "Operation completed. bye..."