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PureEdgeSim

PureEdgeSim: A simulation framework for performance evaluation of cloud, edge and mist computing environments

Please Cite It As (Kindly do not use the github link):

Mechalikh, C., Taktak, H., & Moussa, F. (2021). PureEdgeSim: A simulation framework for performance evaluation of cloud, edge and mist computing environments. Computer Science and Information Systems, 18(1), 43-66.

Bibtex:

@article{mechalikh2021pureedgesim,
  title={PureEdgeSim: A simulation framework for performance evaluation of cloud, edge and mist computing environments},
  author={Mechalikh, Charafeddine and Taktak, Hajer and Moussa, Faouzi},
  journal={Computer Science and Information Systems},
  volume={18}
  number={1},
  pages={43--66},
  year={2021}
}

Version 5.1 Changelog (October 18th 2022)

• Updated the energy model with the ability to set an initial battery level for generated devices, either programmatically or via the xml file.

• Now users can extend the simulation manager to change its behavior, and easily integrate it in the simulation.

• Users can now easily integrate a custom task class.

• All entities are now notified at the end of the simulation through onSimulationEnd() method.

• Failure Model: now tasks can fail due to insufficient resources: RAM, STORAGE, CPU Cores.

• Disabled some unnecessary network links events/loops for better performance.

• Now tasks can be scheduled in batches instead of being scheduled all at once at the beginning of the simulation (hence, decreasing the number of events and therefore, faster simulations and less memory usage).

• Removed unnecessary loops when orchestrating tasks (e.g., no need to browse through edge devices when the cloud only architecture is used, etc.)

• Overall version 5.1 can be 30 times faster and supports up to 8 times the number of tasks compared to previous version 5.0 (see figure below).

• Improved extensibility: Users can now easily incorporate a custom task class, custom simulation manager, as well as a custom topology creator.

• Now applications placement decisions are done after each failure (default behavior, can be changed by extending the DefaultSimulationManager and DefaultOrchestrator classes).

• Reduced duplication and improved code quality.

• Other fixes..

<br></br> <a id="overview"></a>

<br></br> <a id="important"></a>

• <p align="justify">The development and maintenance of this project requires a considerable effort. Thus, any form of contribution is encouraged. </p>
• <p align="justify">If you are willing to use the framework to implement your own project on top of it, creating a fork is a bad solution. You are not supposed to modify the code base to implement your project, but extend it by creating some subclasses. Unless you plan to redistribute your changes, you will end up with an incompatible and obsolete version of the framework. The project is constantly evolving and bug fixes are a priority. Your fork with personal changes will miss these updates and high performance improvements.</p>
• <p align="justify">If you just came across this project and want to check it out later, don't forget to add a star :star: :wink:.</p>

<br></br> <a id="exclusive-features"></a>

• Realistic modeling of edge computing scenarios (and its related computing paradigms).
  • It supports devices heterogeneity (sensors, mobile devices, battery-powered..).
  • It supports the heterogeneity of their means of communication (WiFi, 5G, 4G, Ethernet..).
  • Realistic modeling of computing infrastructure (modeling computational tasks, infrastructure topology...).
  • Realistic network model (Latency, bandwidth allocation, support for peer to peer communications, routing..).
  • Realistic mobility model.
  • Realistic energy model.
• The support for online decision making (tasks orchestration, scheduling, etc.).
• The study of QoS-aware architectures and topologies.
• Efficiency and Scalability.
  • PureEdgeSim supports the simulation of thousands of devices (even +30000 devices).
  • PureEdgeSim supports scenarios with longer simualtion time (even +24 hours).
• A wide collection of metrics
  • Delays: execution time, waiting time, network time.
  • CPU utilization.
  • Network utilization: Average bandwidth, total traffic...
  • Energy consumption: The energy consumption of computing nodes (Cloud, edge/fog servers,edge devices), the remaining energy of battery-powered devices, the energy consumption of WAN, MAN, and LAN networks, and the enrgy consumption of WiFi, Cellular (5G, 4G,..) and Ethernet.
  • Task success rate and reason of failure (the rate of tasks failed due to the lack of resources, due to mobility, due to latency, or due to the death of edge devices when they run out of battery).
  • and many others.
• The visualization of the simulation environement (which helps to debug and understand the course of the simualtion, refer to the figure below).
• Extensibility: Users can implement their scenarios and integrate their models without modifying PureEdgeSim codebase.
• Versatiliy and Wide Applicability
• Ease of use.
• Reliability.
• Correctness.

Efficiency and Scalability

A 10-minute simulation scenario with 10000 devices (which generated 1131000 tasks) took 58 seconds.

A 24-hour simulation scenario with 200 devices (total of 3221280 generated tasks generated) took 51 seconds.

A 60-minute scenario with 100 devices took only 1 seconds.

Simulation Accuracy

Versatiliy and Wide Applicability

<?xml version="1.0"?>
<edge_devices>
	<device> <!-- this is a smartphone, for example -->
		<mobility>true</mobility><!-- the device is mobile or fixed --> 
		<connectivity>4g</connectivity><!--the connectivity type 4g, ethernet, Wi-Fi-->
		<speed>1.4</speed><!-- the speed of the device in meters per second : 1.4m/s = 5km/h, 0 = non mobile-->
		<minPauseDuration>100</minPauseDuration><!-- the minimum delay before moving to a new location-->
		<maxPauseDuration>400</maxPauseDuration><!-- the maximum delay before moving to a new location-->
		<minMobilityDuration>60</minMobilityDuration><!-- the minimum delay before stopping-->
		<maxMobilityDuration>100</maxMobilityDuration><!-- the maximum delay before stopping-->
		<battery>true</battery> <!-- relies on battery? -->
		<percentage>30</percentage> <!-- percentage of this device type -->
		<batteryCapacity>18.75</batteryCapacity> <!-- battery capacity in Watt-Hour -->
		<idleConsumption>0.078</idleConsumption><!-- idle energy consumption/ 
			second-->
		<maxConsumption>3.3</maxConsumption><!-- max energy consumption/second, 
			when device cpu is use at 100%-->
		<isOrchestrator>false</isOrchestrator> <!-- whether it orchestrates tasks or not -->
		<canGenerateTasks>true</canGenerateTasks> <!-- whether it generates data/tasks or not -->
		<canProcessTasks>true</canProcessTasks> <!-- whether it can process data/tasks or not -->
		<hosts>
			<host>
				<core>8</core> <!-- Number of CPU cores -->
				<mips>25000</mips> <!-- Processing power in MIPS -->
				<ram>4096</ram> <!-- Ram capacity in MBytes -->
				<storage>128000</storage> <!-- Storage in MBytes -->
				<VMs
					<VM>
						<core>8</core>
						<mips>25000</mips>
						<ram>4096</ram>
						<storage>128000</storage> 
					</VM>
				</VMs>
			</host>
		</hosts>
	</device>
...
</edge_devices>

Ease of Use

Extensibility

//Create a PureEdgeSim simulation
Simulation sim = new Simulation();

// To use your custom mobility model instead of the default one:
sim.setCustomMobilityModel(YourCustomMobilityModel.class);

// To change the tasks orchestrator:
sim.setCustomEdgeOrchestrator(YourCustomOrchestrator.class);
		
// To change the computing node class:
sim.setCustomComputingNode(YourCustomComputingNode.class);
		
// To change the tasks generator:
sim.setCustomTaskGenerator(YourCustomTaskGenerator.class); 
		
// To change the network model
sim.setCustomNetworkModel(YourCustomNetworkModel.class); 
		
// To change the simulation manager
sim.setCustomSimulationManager(YourCustomSimulationManager.class); 

// To change the topology
sim.setCustomTopologyCreator(YourCustomTopologyCreator.class); 
		
/* to use the default models you can simply delete or comment those lines */

// Finally, you can launch the simulation
sim.launchSimulation();

Correctness

Reliability

<br></br> <a id="how-to-use"></a>

Using an IDE

1. Downloading and extracting the project source code archive from the project repository : This can be done using the web browser or the command line git clone https://github.com/CharafeddineMechalikh/PureEdgeSim.git.
2. <p align="justify">Importing the project to the IDE:</P>

• <p align="justify">In NetBeans, simply select the "Open project" option and chose the project directory.</P>
• <p align="justify">In Eclipse or IntelliJ IDEA, the project must be imported by selecting the location where it was extracted or cloned.</p>

3. <p align="justify">The imported project consists of ten packages: the main application package, the above-mentioned modules, and the examples package. The main application and the modules are the simulator source code that normally should not be modified. The examples, however, are where the users can start.</p>
4. <p align="justify">It is necessary to convert the project into a Maven project in order to download all the required libraries.</p>
5. <p align="justify">Once all the necessary libraries are downloaded, users can start with the most basic examples by running any of the classes located in the <a href="https://github.com/CharafeddineMechalikh/PureEdgeSim/tree/master/PureEdgeSim/examples">“examples“</a> package.</p>
6. <p align="justify">To build a new simulation scenario, the simplest way is to create another class in this package.</p>

Adding PureEdgeSim as a Dependency

It is possible to use PureEdgeSim as a dependency in a Maven project, by inserting the dependency below into the pom.xml file:

<dependency>
	<groupId>com.mechalikh</groupId>
	<artifactId>pureedgesim</artifactId>
	<version>5.1.0</version>
</dependency>

Or on Gradle :

dependencies {
 implementation 'com.mechalikh:pureedgesim:5.1.0'
}

Via Command Line

Assuming that git and maven are already installed, PureEdgeSim can be run from the command line as follows:

1. First, the project source code must be downloaded by cloning the repository via the command git clone https://github.com/CharafeddineMechalikh/PureEdgeSim.git.
2. Now that the project is cloned, it can be built using Maven by executing the mvn clean install command in the directory where it was cloned.
3. Now, the examples can be executed on Windows, Linux, or Mac operating systems, using the command mvn exec:java -Dexec.mainClass="package.Class_Name" . For instance, to execute “Example1”, the command is mvn exec:java -Dexec.mainClass="examples.Example1"

<br></br> <a id="related-work"></a>

Rienforcement and Deep Learning Scenarios

1. <p align="justify">Mechalikh, C., Taktak, H., & Moussa, F. (2020, April). A fuzzy decision tree based tasks orchestration algorithm for edge computing environments. In International Conference on Advanced Information Networking and Applications (pp. 193-203). Springer, Cham. <a href="https://github.com/CharafeddineMechalikh/FDT_based_workload_orchestration">The algorithm can be found here</a>.</p>
2. <p align="justify">Safavifar, Z., Ghanadbashi, S., & Golpayegani, F. (2021, November). Adaptive Workload Orchestration in Pure Edge Computing: A Reinforcement-Learning Model. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 856-860). IEEE.</p>
3. <p align="justify">Neelakantam, G., Onthoni, D. D., & Sahoo, P. K. (2020). Reinforcement learning based passengers assistance system for crowded public transportation in fog enabled smart city. Electronics, 9(9), 1501.</p>
4. <p align="justify">Iftikhar, S., Golec, M., Chowdhury, D., Gill, S. S., & Uhlig, S. (2022). FogDLearner: A Deep Learning-based Cardiac Health Diagnosis Framework using Fog Computing. In Australasian Computer Science Week 2022 (pp. 136-144).</p>

Satellite Edge Computing

5. <p align="justify">Wei, J., Cao, S., Pan, S., Han, J., Yan, L., & Zhang, L. (2020, June). SatEdgeSim: A toolkit for modeling and simulation of performance evaluation in satellite edge computing environments. In 2020 12th International Conference on Communication Software and Networks (ICCSN) (pp. 307-313). IEEE.</p>
6. <p align="justify">Qin, J., Guo, X., Ma, X., Li, X., & Yang, J. (2022). Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing. Aerospace, 9(8), 451.</p>

Data Caching

7. <p align="justify">Mechalikh, C., Taktak, H., & Moussa, F. (2019, July). Towards a scalable and QoS-aware load balancing platform for edge computing environments. In 2019 International Conference on High Performance Computing & Simulation (HPCS) (pp. 684-691). IEEE.</p>
8. <p align="justify">Epifâneo, L., Correia, C., & Rodrigues, L. (2021, November). Cathode: A Consistency-Aware Data Placement Algorithm for the Edge. In 2021 IEEE 20th International Symposium on Network Computing and Applications (NCA) (pp. 1-10). IEEE.</p>

Task Scheduling and Security

9. Tiburski, R. T. (2021). Task scheduling and security for edge devices in internet of things applications.

Self-Organized Architectures

10. <p align="justify">Mechalikh, C., Taktak, H., & Moussa, F. (2019, June). A scalable and adaptive tasks orchestration platform for IoT. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) (pp. 1557-1563). IEEE.</p>
11. <p align="justify">Mordacchini, M., Ferrucci, L., Carlini, E., Kavalionak, H., Coppola, M., & Dazzi, P. (2021, September). Self-organizing Energy-Minimization Placement of QoE-Constrained Services at the Edge. In International Conference on the Economics of Grids, Clouds, Systems, and Services (pp. 133-142). Springer, Cham.</p>

Software-Defined Networking

12. <p align="justify">Okwuibe, J., Haavisto, J., Kovacevic, I., Harjula, E., Ahmad, I., Islam, J., & Ylianttila, M. (2021). SDN-Enabled Resource Orchestration for Industrial IoT in Collaborative Edge-Cloud Networks. IEEE Access, 9, 115839-115854.</p>

Latency-Aware Application Placement

13. <p align="justify">Khosroabadi, F., Fotouhi-Ghazvini, F., & Fotouhi, H. (2021). SCATTER: service placement in real-time fog-assisted IoT networks. Journal of Sensor and Actuator Networks, 10(2), 26.</p>
14. <p align="justify">Ferrucci, L., Mordacchini, M., Coppola, M., Carlini, E., Kavalionak, H., & Dazzi, P. (2020, June). Latency preserving self-optimizing placement at the edge. In Proceedings of the 1st Workshop on Flexible Resource and Application Management on the Edge (pp. 3-8).</p>

Energy-Aware Application Placement

15. <p align="justify">Kavalionak, H., Coppola, M., & Dazzi, P. Self-organizing Energy-Minimization Placement of QoE-Constrained Services at the Edge. In Economics of Grids, Clouds, Systems, and Services: 18th International Conference, GECON 2021, Virtual Event, September 21–23, 2021, Proceedings (p. 133). Springer Nature.</p>
16. <p align="justify">Mordacchini, M., Ferrucci, L., Carlini, E., Kavalionak, H., Coppola, M., & Dazzi, P. (2022). Energy and QoE aware Placement of Applications and Data at the Edge.</p>

Other Use Cases

17. <p align="justify">Kaneko, Y., Yokoyama, Y., Monma, N., Terashima, Y., Teramoto, K., Kishimoto, T., & Saito, T. (2020, September). A Microservice-Based Industrial Control System Architecture Using Cloud and MEC. In International Conference on Edge Computing (pp. 18-32). Springer, Cham.</p>
18. <p align="justify">Javaid, H., Saleem, S., Wajid, B., & Khan, U. G. (2021, May). Diagnose a Disease: A Fog Assisted Disease Diagnosis Framework with Bidirectional LSTM. In 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) (pp. 1-6). IEEE.</p>

<br></br> <a id="license"></a>

This project is licensed under GNU GPLv3.