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 cloud simulators in the research and academia. CloudSim is completely written in Java.

CloudSim extensions

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

  • Though CloudSim itself does not have a graphical user interface, extensions such as CloudReports[3] offer a GUI for CloudSim simulations.
  • CloudSimEx[4] extends CloudSim by adding MapReduce simulation capabilities and parallel simulations.
  • Cloud2Sim[5][6] extends CloudSim to execute on multiple distributed servers, by leveraging Hazelcast distributed execution framework.
  • RECAP DES[7][8][9] extends the CloudSimPlus extension to model synchronous hierarchical architectures (such as ElasticSearch).
  • ThermoSim[10][11] 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.
  2. "The Cloud Computing and Distributed Systems (CLOUDS) Laboratory, University of Melbourne".
  3. Sá, Thiago Teixeira; Calheiros, Rodrigo N.; Gomes., Danielo G. (2014). CloudReports: An Extensible Simulation Tool for Energy-Aware Cloud Computing Environments. In Cloud Computing, Springer International Publishing. Computer Communications and Networks. pp. 127–142. doi:10.1007/978-3-319-10530-7_6. ISBN 978-3-319-10529-1.
  4. "CloudSimEx Project". 2018-08-06.
  5. 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.
  6. 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.
  7. "RECAP DES repository".
  8. 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.
  9. 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.
  10. "ThermoSim repository".
  11. 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.
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