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

scripts/sample_app5/ai_trainer/.gitignore

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+*.class

scripts/sample_app5/ai_trainer/README.md

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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

scripts/sample_app5/ai_trainer/WekaModelCreator.java

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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;
+	}
+}

scripts/sample_app5/ai_trainer/config.json

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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"
+} 

scripts/sample_app5/ai_trainer/data_convertor.py

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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 ("##############################################################")
+

scripts/sample_app5/ai_trainer/generate_training_data.sh

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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 

scripts/sample_app5/ai_trainer/generate_weka_models.sh

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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