Predicting hot-spots in distributed cloud databases using association rule mining

Joarder Mohammad Mustafa Kamal, Manzur Murshed, Mohamed Medhat Gaber

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    Abstract

    Data partitioning is a popular technique to horizontally or vertically split table attributes of a Cloud database cluster to evenly distribute increasing workloads. However, hot-spots can be created due to inappropriate partitioning scheme and static partition management without considering the dynamic workload characteristics. In this paper, an automatic database partition management scheme - APM - is proposed which periodically analyses workload logs to predict the formation of any potential hot-spot using association rule mining. A detailed illustration of the proposed scheme is presented with examples along with a cost model following by experimental observations from running a HBase cluster with YCSB workloads in AWS.

    Original languageEnglish
    Title of host publicationProceedings - 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, UCC 2014
    Place of PublicationDanvers MA USA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages800-805
    Number of pages6
    ISBN (Electronic)9781479978816
    DOIs
    Publication statusPublished - 29 Jan 2014
    EventIEEE/ACM International Conference on Utility and Cloud Computing 2014 - London, United Kingdom
    Duration: 8 Dec 201411 Dec 2014
    Conference number: 7th

    Conference

    ConferenceIEEE/ACM International Conference on Utility and Cloud Computing 2014
    Abbreviated titleUCC 2014
    CountryUnited Kingdom
    CityLondon
    Period8/12/1411/12/14

    Keywords

    • Association rule mining
    • Distributed database
    • Hot-spots
    • Partitioning
    • Workload

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