Achieving Elasticity for Cloud MapReduce Jobs

Khaled Salah, Jose Alcaraz Calero

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

    9 Citations (Scopus)

    Abstract

    These days, both the cloud computing paradigm and MapReduce programming framework have become key enablers for running big data analytics and large-scale compute-and data-intensive applications. Achieving proper elasticity for cloud MapReduce jobs is a critical research problem that has been overlooked. In this paper, we focus on how to achieve proper elasticity for MapReduce jobs when executed on cloud clusters. In particular, we present an analytical queueing model that can be used to determine at any given time and under different workload conditions the minimal number of mappers and reducers needed to satisfy the Service Level Objective (SLO) response time.
    Original languageEnglish
    Title of host publicationPROCEEDINGS OF THE 2013 IEEE 2ND INTERNATIONAL CONFERENCE ON CLOUD NETWORKING (CLOUDNET)
    EditorsX Fu, P Sharma, D Huang, D Medhi
    PublisherIEEE
    Pages195-199
    ISBN (Print)978-1-4799-0568-3
    DOIs
    Publication statusPublished - 2013

    Publication series

    NameIEEE International Conference on Cloud Networking
    PublisherIEEE
    ISSN (Print)2374-3239

    Keywords

    • MapReduce
    • Cloud Computing
    • Elasticity
    • Netwrok and Sevice Delays
    • Queueing Analysis
    • Performance

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