Adaptive virtual machine migration mechanism for energy efficiency

S. Sohrabi, A. Tang, I. Moser, A. Aleti

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    15 Citations (Scopus)


    Cloud systems have become a popular platform for business applications due to the flexibility in usage and payment they offer. One of the caveats of Cloud systems is their high energy consumption. Minimizing energy consumption while maintaining a high service level has become a relevant optimization task for Cloud providers. Opportunities for energy savings arise when server hosts are overloaded, which also entails unnecessary delays.

    To address the problem, researchers have devised strategies how to choose the server host to deploy an application to and how to choose a running application for migration when a host has been identified as overloaded. In this work, we introduce a Bayesian Belief Network which learns over time which of the virtual machines are best removed from a host that has been identified as overloaded. The probabilistic choice is made among virtual machines that are grouped by their degree of CPU usage. Given the feedback in the form of the computing resources saved, the system learns which virtual machine profiles should be shifted for best performance. This strategy compares favourably to two existing methods for load balancing.
    Original languageEnglish
    Title of host publication5th International Workshop on Green and Sustainable Software (GREENS 2016)
    Subtitle of host publication16 May 2016, Austin, Texas, USA, Proceedings
    Place of PublicationNew York, New York
    PublisherAssociation for Computing Machinery (ACM)
    Number of pages7
    ISBN (Print)9781450341615
    Publication statusPublished - 14 May 2016
    EventInternational Workshop on Green and Sustainable Software 2016 - Austin, United States of America
    Duration: 16 May 201616 May 2016
    Conference number: 5th


    WorkshopInternational Workshop on Green and Sustainable Software 2016
    Abbreviated titleGREENS 2016
    Country/TerritoryUnited States of America
    Internet address


    • Bayesian network
    • cloud computing
    • VM migration

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