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CloudAnalyst: A CloudSim-Based Visual Modeller for Analysing Cloud Computing Environments and Applications

Published: 20 April 2010 Publication History

Abstract

Advances in Cloud computing opens up many new possibilities for Internet applications developers. Previously, a main concern of Internet applications developers was deployment and hosting of applications, because it required acquisition of a server with a fixed capacity able to handle the expected application peak demand and the installation and maintenance of the whole software infrastructure of the platform supporting the application. Furthermore, server was underutilized because peak traffic happens only at specific times. With the advent of the Cloud, deployment and hosting became cheaper and easier with the use of pay-peruse flexible elastic infrastructure services offered by Cloud providers. Because several Cloud providers are available, each one offering different pricing models and located in different geographic regions, a new concern of application developers is selecting providers and data center locations for applications. However, there is a lack of tools that enable developers to evaluate requirements of large-scale Cloud applications in terms of geographic distribution of both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of Cloud environments and applications, we propose CloudAnalyst. It was developed to simulate large-scale Cloud applications with the purpose of studying the behavior of such applications under various deployment configurations. CloudAnalyst helps developers with insights in how to distribute applications among Cloud infrastructures and value added services such as optimization of applications performance and providers incoming with the use of Service Brokers.

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  • (2024)FootPrinter: Quantifying Data Center Carbon FootprintCompanion of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629527.3651419(189-195)Online publication date: 7-May-2024
  • (2023)Prediction-based scheduling techniques for cloud data center’s workload: a systematic reviewCluster Computing10.1007/s10586-023-04024-826:5(3209-3235)Online publication date: 18-May-2023
  • (2022)Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future DirectionsACM Computing Surveys10.1145/351300254:11s(1-38)Online publication date: 9-Sep-2022
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  1. CloudAnalyst: A CloudSim-Based Visual Modeller for Analysing Cloud Computing Environments and Applications

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      Published In

      cover image Guide Proceedings
      AINA '10: Proceedings of the 2010 24th IEEE International Conference on Advanced Information Networking and Applications
      April 2010
      1335 pages
      ISBN:9780769540184

      Publisher

      IEEE Computer Society

      United States

      Publication History

      Published: 20 April 2010

      Author Tags

      1. Cloud Computing
      2. Modeling
      3. Simulation

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      • (2024)FootPrinter: Quantifying Data Center Carbon FootprintCompanion of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629527.3651419(189-195)Online publication date: 7-May-2024
      • (2023)Prediction-based scheduling techniques for cloud data center’s workload: a systematic reviewCluster Computing10.1007/s10586-023-04024-826:5(3209-3235)Online publication date: 18-May-2023
      • (2022)Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future DirectionsACM Computing Surveys10.1145/351300254:11s(1-38)Online publication date: 9-Sep-2022
      • (2022)Towards Metaheuristic Scheduling Techniques in Cloud and Fog: An Extensive Taxonomic ReviewACM Computing Surveys10.1145/349452055:3(1-43)Online publication date: 3-Feb-2022
      • (2019)Realm Towards Service Optimization in Fog ComputingInternational Journal of Fog Computing10.4018/IJFC.20190701022:2(13-43)Online publication date: 1-Jul-2019
      • (2019)Load balancing in cloud computing using water flow-like algorithmProceedings of the Second International Conference on Data Science, E-Learning and Information Systems10.1145/3368691.3368720(1-6)Online publication date: 2-Dec-2019
      • (2019)Issues and Challenges of Load Balancing Techniques in Cloud ComputingACM Computing Surveys10.1145/328101051:6(1-35)Online publication date: 4-Feb-2019
      • (2018)Effective Management of Data Centers Resources for Load Balancing in Cloud ComputingInternational Journal of Information Retrieval Research10.4018/IJIRR.20180401038:2(40-56)Online publication date: 1-Apr-2018
      • (2018)TrustyFeerWireless Communications & Mobile Computing10.1155/2018/10732162018Online publication date: 25-Feb-2018
      • (2018)A Survey and Taxonomy of Self-Aware and Self-Adaptive Cloud Autoscaling SystemsACM Computing Surveys10.1145/319050751:3(1-40)Online publication date: 12-Jun-2018
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