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Balancing performance, resource efficiency and energy efficiency for virtual machine deployment in DVFS-enabled clouds: an evolutionary game theoretic approach

Published: 12 July 2014 Publication History

Abstract

This paper proposes and evaluates a multiobjective evolutionary game theoretic framework for adaptive and stable application deployment in clouds that support dynamic voltage and frequency scaling (DVFS) for CPUs. The proposed framework, called Cielo, aids cloud operators to adapt the resource allocation to applications and their locations according to the operational conditions in a cloud (e.g., workload and resource availability) with respect to multiple conflicting objectives such as response time performance, recourse utilization and power consumption. Moreover, Cielo theoretically guarantees that each application performs an evolutionarily stable deployment strategy, which is an equilibrium solution under given operational conditions. Simulation results verify this theoretical analysis; applications seek equilibria to perform adaptive and evolutionarily stable deployment strategies. Cielo allows applications to successfully leverage DVFS to balance their response time performance, resource utilization and power consumption.

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Cited By

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  • (2020)CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for CloudsIEEE Access10.1109/ACCESS.2020.29852348(62990-63003)Online publication date: 2020
  • (2019)SLA-Aware Best Fit Decreasing Techniques for Workload Consolidation in CloudsIEEE Access10.1109/ACCESS.2019.29411457(135256-135267)Online publication date: 2019
  • (2018)Distributed Resource Allocation for Data Center Networks: A Hierarchical Game ApproachIEEE Transactions on Cloud Computing10.1109/TCC.2018.2829744(1-1)Online publication date: 2018
  • Show More Cited By

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    cover image ACM Conferences
    GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
    July 2014
    1524 pages
    ISBN:9781450328814
    DOI:10.1145/2598394
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 12 July 2014

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    Author Tags

    1. cloud computing
    2. evolutionary game theory
    3. multiobjective optimization
    4. power-aware virtual machine placement

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    GECCO '14
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    GECCO '14: Genetic and Evolutionary Computation Conference
    July 12 - 16, 2014
    BC, Vancouver, Canada

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    GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    Cited By

    View all
    • (2020)CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for CloudsIEEE Access10.1109/ACCESS.2020.29852348(62990-63003)Online publication date: 2020
    • (2019)SLA-Aware Best Fit Decreasing Techniques for Workload Consolidation in CloudsIEEE Access10.1109/ACCESS.2019.29411457(135256-135267)Online publication date: 2019
    • (2018)Distributed Resource Allocation for Data Center Networks: A Hierarchical Game ApproachIEEE Transactions on Cloud Computing10.1109/TCC.2018.2829744(1-1)Online publication date: 2018
    • (2018)SLA-Aware Energy Efficient Resource Management for Cloud EnvironmentsIEEE Access10.1109/ACCESS.2018.28083206(15004-15020)Online publication date: 2018
    • (2015)Performance Evaluation of Energy-Aware Best Fit Decreasing Algorithms for Cloud EnvironmentsProceedings of the 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS)10.1109/DSDIS.2015.104(464-469)Online publication date: 11-Dec-2015

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