FEAST

A Framework for Evaluating Implementation Architectures of Self-adaptive IoT Systems

This repository is the official implementation of FEAST: A Framework for Evaluating ImplementationArchitectures of Self-adaptive IoT Systems paper.

Installation Requirements

1. Install the latest version of JAVA - https://www.java.com/en/download/
2. Install Apache Kafka - https://kafka.apache.org/quickstart
3. Install kafka-python - https://pypi.org/project/kafka-python/
4. Install Keras in Python (Tested with version 2.2.4) - https://keras.io
5. Install Tensorflow in Python (Tested with version 1.15) - https://www.tensorflow.org/install
6. Install Scikit-learn in Python (Tested with version 0.22) - https://scikit-learn.org/

Project Description

Implementation is the folder that contains the implementation of the case study in the document and is organized as follows:

• Components: contains all the basic components and connectors that the specific implementations use. It therefore contains all the basic code of each MAPE-K loop activity, of the various connectors and of the utility classes necessary for the experiment.
• Layered: this folder contains the components of the MAPE loop, the CupCarbon sample project and the Machine Learning models of the Layered Implementation (Socket based strategy).
• BB: this folder contains the components of the MAPE loop, the CupCarbon sample project and the Machine Learning models of the Blackboard Implementation (Centralized strategy).
• EventDriven: this folder contains the components of the MAPE loop, the CupCarbon sample project and the Machine Learning models of the Event Driven Implementation (Publish Subscribe based strategy).
• results and STATS folders contain some organized results obtained by performing the experiments.

DataAnalyzer folder contains all the code that has been developed for the purposes of the analysis and evaluation phase of the framework. In particular, it has the following organization:

• 24h folder: results obtained by the simulation of the system performed on Scenario 1
• 2h folder: results obtained by the simulation of the system performed on Scenario 2 (see the paper for details of scenarios)
• DataLoader.py: this class contains all the functions able to load the data from the files and generate runtime data structures. This class contains the utility application statement too.
• UtilityAnalizer.py: class which contains all the functions to make the analysis and the evaluation
• evaluation.py: class to launch in order to perform the analysis and print the data. In this class we can set "RT_Thresh" (threshold for response time) and "E_Thresh" (threshold for energy consumption) parameters for the utility function.

CupCarbon folder contains the customized source code of CupCarbon to perform the experiment.

Configuration

To be able to perform the experiments it is necessary to make some configurations first.

• The communication of the components in the socket based strategy implementation was made through sockets. Please check the availability of the 65431, 65432, 65433 ports.
• The communication of the components in the centralized implementation was made through MySQL tables. Create those tables and edit the configuration data in the scripts \Implementation\BB\BB_*.py.
• The communication of the components in the publish subscriube based strategy implementation was made through Kafka producers and consumers. They use the following topics: BatchDataTopic, CupCarbonEventsTopic, PlannerExecutorTopic. Please create them before running the experiment.

Running Experiment

To run properly the experiment please do:

• You have to start from the same configuration for each implementaiton, so edit the config.txt file so that it is the same for everyone before starting
• Set the verbose flag of each script to the same value (0 or 1) otherwise the response time will be measured differently between scripts
• Clean the MySQL tables if they contain old data
• In the /CupCarbon-master_4.0/src/senscript_functions/Functions.java edit the myf function filename parameter putting the path of the file which you want to read the adaptation values (config.txt). Take care of putting the correct path for the running of each implementation.
• Launch the customized CupCarbon source code and load the NdR_CO project inside the correct implementation folder
• Change the CupCarbon parameters in order to put the correct execution scenario
• Launch the MAPE-K scripts of the correct implementaion but the XX_Monitor.py one. Make sure that the others are connected.
• Start the CupCarbon simulation
• Start the XX_Monitor.py script

Let __IMPLFLD__ the folder of the chosen implementation (Layered, BB or EventDriven). The energy results will be in the __IMPLFLD__\NdR_CO\results\wisen_simulation.csv file. The response time and CPU time results will be under __IMPLFLD__\perf_data folder.