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CloudSim

Cloud computing simulator From Wikipedia, the free encyclopedia

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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

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CloudSim extensions

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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.
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References

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