Using Performance Forecasting to Accelerate Elasticity

Paulo Moura, Fabio Kon, Spyros Voulgaris, Martinus Richardus van Steen

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    68 Downloads (Pure)

    Abstract

    Cloud computing facilitates dynamic resource provisioning. The automation of resource management, known as elasticity, has been subject to much research. In this context, monitoring of a running service plays a crucial role, and adjustments are made when certain thresholds are crossed. On such occasions, it is common practice to simply add or remove resources. In this paper we investigate how we can predict the performance of a service to dynamically adjust allocated resources based on predictions. In other words, instead of “repairing‿ because a threshold has been crossed, we attempt to stay ahead and allocate an optimized amount of resources in advance. To do so, we need to have accurate predictive models that are based on workloads. We present our approach, based on the Universal Scalability Law, and discuss initial experiments.
    Original languageUndefined
    Title of host publicationProceedings of the Second International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing (ARMS-CC 2015), Revised Selected Papers
    Place of PublicationBerlin
    PublisherSpringer
    Pages17-31
    Number of pages15
    ISBN (Print)978-3-319-28447-7
    DOIs
    Publication statusPublished - Jul 2015
    EventSecond International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, ARMS-CC 2015 - Donostia, Spain
    Duration: 20 Jul 201520 Jul 2015

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    Volume9438
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Workshop

    WorkshopSecond International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, ARMS-CC 2015
    Period20/07/1520/07/15
    Other20 July 2015

    Keywords

    • EWI-26880
    • scalabilitymodeling
    • Elasticity
    • METIS-316852
    • IR-100082
    • Cloud computing
    • Performance Prediction

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