Energy-efficient adaptive virtual machine migration mechanism for private Clouds

Sahar Sohrabi, Yun Yang, Irene Moser, Aldeida Aleti

    Research output: Contribution to journalArticleResearchpeer-review

    1 Citation (Scopus)

    Abstract

    Private Cloud provides Cloud services with its relatively limited resources compared to public Clouds. Resources in private Clouds should be used energy efficiently. The resource utilization is determined by the assignment of virtual machines (VMs) to hosts. Because of the frequent changes in the resource requests on VMs, the system might become imbalanced, some hosts are overloaded/underloaded. Virtual machine migration is a solution to ease the imbalance problem. Virtual machine migration includes the selection of a VM for migration and decision upon where it should be taken to (mapped). In this work, VM selection and VM mapping are integrated and aimed to ease the imbalance problem for energy efficiency. Moreover, selection and mapping have become adaptive to ease imbalance, while optimizing energy consumption and adaptively responding to changes in the system. Our proposed adaptive mechanism applies Bayesian inference to estimate the likelihood of a VM migration decision, both VM selection for migration and VM mapping, optimizing energy consumption. The proposed mechanism is evaluated on CloudSim using PlanetLab workload on a heterogeneous Cloud. It is demonstrated to reduce energy consumption significantly, (on average) by 116%, while its total execution time is also, (on average) 5.39 times, shorter than the competing state-of-the-art policies.

    Original languageEnglish
    Article numbere4170
    Number of pages10
    JournalConcurrency and Computation-Practice & Experience
    Volume29
    Issue number18
    DOIs
    Publication statusPublished - 25 Sep 2017

    Keywords

    • Adaptive
    • Bayesian inference
    • Bayesian probabilities
    • Cloud computing
    • CloudSim
    • Energy efficiency

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