CloudSim

CloudSim is a framework for modeling and simulation of cloud computing infrastructures and services. ^[1] Originally built primarily at the Cloud Computing and Distributed Systems (CLOUDS) Laboratory, ^[2] the University of Melbourne, Australia, CloudSim has become one of the most popular open source ^{[ citation needed ]} cloud simulators in the research and academia. CloudSim is completely written in Java. The latest version of CloudSim is CloudSim v6.0.0-beta on GitHub. ^[3] Cloudsim is suitable for implemeting simulations scenarios based on Infrastructure as a service as well as with latest version Platform as a service, so get started here

CloudSim extensions

Initially developed as a stand-alone cloud simulator, CloudSim has further been extended by independent researchers.

• GPUCloudSim ^{[4] [5] [6]} is an enhanced CloudSim tool for modeling GPU-based cloud infrastructures and data centers. It offers simulations for multi-GPU setups, customizable GPU policies, GPU remoting, etc. It also examines performance impacts and interactions within virtualized GPU environments.

• CloudSim Plus ^{[7] [8]} is a totally re-engineered CloudSim fork providing general-purpose cloud computing simulation and exclusive features such as: multi-cloud simulations, vertical and horizontal VM scaling, host fault injection and recovery, joint power- and network-aware simulations and more.
• Though CloudSim itself does not have a graphical user interface, extensions such as CloudReports ^[9] offer a GUI for CloudSim simulations.
• CloudSimEx ^[10] extends CloudSim by adding MapReduce simulation capabilities and parallel simulations.
• Cloud2Sim ^{[11] [12]} extends CloudSim to execute on multiple distributed servers, by leveraging Hazelcast distributed execution framework.
• RECAP DES ^{[13] [14] [15]} extends the CloudSim Plus framework to model synchronous hierarchical architectures (such as ElasticSearch).
• ThermoSim ^{[16] [17]} extends CloudSim toolkit by incorporating thermal characteristics, and uses Deep learning-based temperature predictor for cloud nodes.

References

1. Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011). "CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms" (PDF). Software: Practice and Experience. 41 (1): 23–50. doi:10.1002/spe.995. hdl:10923/23235. S2CID   14970692.
2. "The Cloud Computing and Distributed Systems (CLOUDS) Laboratory, University of Melbourne".
3. "CloudSimE". GitHub . 2 February 2023.
4. "GPUCloudSim GitHub". GitHub . 1 December 2023.
5. Siavashi, A., Momtazpour, M. (2019). "GPUCloudSim: an extension of CloudSim for modeling and simulation of GPUs in cloud data centers". Journal of Supercomputing, 75, 2535–2561.
6. Siavashi, A.; Momtazpour, M. (2023). "gVMP: A multi-objective joint VM and vGPU placement heuristic for API remoting-based GPU virtualization and disaggregation in cloud data centers" . Journal of Parallel and Distributed Computing. 172: 97–113. doi:10.1016/j.jpdc.2022.10.008. ISSN   0743-7315.
7. "CloudSim Plus Project". 28 October 2021.
8. Silva Filho, Manoel; Oliveira, Raysa; Inácio, Pedro; Freire, Mario (8–12 May 2017). CloudSim Plus: a Cloud Computing Simulation Framework Pursuing Software Engineering Principles for Improved Modularity, Extensibility and Correctness. IFIP/IEEE International Symposium on Integrated Network Management, 2017. Lisbon. p. 7. doi:10.23919/INM.2017.7987304.
9. Sá, Thiago Teixeira; Calheiros, Rodrigo N.; Gomes., Danielo G. (2014). "CloudReports: An Extensible Simulation Tool for Energy-Aware Cloud Computing Environments". Cloud Computing. Computer Communications and Networks. In Cloud Computing, Springer International Publishing. pp. 127–142. doi:10.1007/978-3-319-10530-7_6. ISBN   978-3-319-10529-1.
10. "CloudSimEx Project". GitHub . 6 August 2018.
11. Kathiravelu, Pradeeban; Veiga, Luís (9 September 2014). Concurrent and Distributed CloudSim Simulations. IEEE 22nd International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS). Paris. pp. 490–493. doi:10.1109/MASCOTS.2014.70.
12. Kathiravelu, Pradeeban; Veiga, Luís (8 December 2014). An Adaptive Distributed Simulator for Cloud and MapReduce Algorithms and Architectures. IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC), 2014. London. pp. 79–88. doi:10.1109/UCC.2014.16.
13. "RECAP DES repository".
14. M. Bendechache, S. Svorobej, P. T. Endo, M. Marino, E. Ares, J. Byrne and T. Lynn, "Modelling and Simulation of ElasticSearch using CloudSim," International Symposium on Distributed Simulation and Real Time Applications, 2019.
15. M. Bendechache, I. Silva, G. Santos, A. Guedes, S. Svorobej, M. Marino, E. Ares, J. Byrne, P. T. Endo and T. Lynn, "Analysing dependability and performance of a real-world Elastic Search application," Latin-America Symposium on Dependable Computing, 2019.
16. Gill, Sukhpal Singh; Tuli, Shreshth; Toosi, Adel Nadjaran; Cuadrado, Felix; Garraghan, Peter; Bahsoon, Rami; Lutfiyya, Hanan; Sakellariou, Rizos; Rana, Omer; Dustdar, Schahram; Buyya, Rajkumar (August 2020). "ThermoSim repository". Journal of Systems and Software. 166: 110596. arXiv: 2004.08131 . doi:10.1016/j.jss.2020.110596. S2CID   215814095.
17. Sukhpal Singh Gill, Shreshth Tuli, Adel Nadjaran Toosi, Felix Cuadrado, Peter Garraghan, Rami Bahsoon, Hanan Lutfiyya, Rizos Sakellariou, Omer Rana, Schahram Dustdar, and Rajkumar Buyya, ThermoSim: Deep Learning based Framework for Modeling and Simulation of Thermal-aware Resource Management for Cloud Computing Environments, Journal of Systems and Software (JSS), Volume 166, Pages: 1–20, ISSN   0164-1212, Elsevier Press, Amsterdam, the Netherlands, August 2020.

External links

• Official website