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 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.
^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.
^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.
^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.
^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.
^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.
^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,
ISSN0164-1212, Elsevier Press, Amsterdam, the Netherlands, August 2020.