Detecting Performance Degradation of Software-Intensive Systems in the Presence of Trends and Long-Range Dependence

Alexey Artemov, Evgeny Burnaev

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

    10 Citations (Scopus)

    Abstract

    As contemporary software-intensive systems reach increasingly large scale, it is imperative that failure detection schemes be developed to help prevent costly system downtimes. A promising direction towards the construction of such schemes is the exploitation of easily available measurements of system performance characteristics such as average number of processed requests and queue size per unit of time. In this work, we investigate a holistic methodology for detection of abrupt changes in time series data in the presence of quasi-seasonal trends and long-range dependence with a focus on failure detection in computer systems. We propose a trend estimation method enjoying optimality properties in the presence of long-range dependent noise to estimate what is considered 'normal' system behaviour. To detect change-points and anomalies, we develop an approach based on the ensembles of 'weak' detectors. We demonstrate the performance of the proposed change-point detection scheme using an artificial dataset, the publicly available Abilene dataset as well as the proprietary geoinformation system dataset.

    Original languageEnglish
    Title of host publicationProceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
    EditorsCarlotta Domeniconi, Francesco Gullo, Francesco Bonchi, Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Zhi-Hua Zhou, Xindong Wu
    PublisherIEEE Computer Society
    Pages29-36
    Number of pages8
    ISBN (Electronic)9781509054725
    DOIs
    Publication statusPublished - 2 Jul 2016
    Event16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain
    Duration: 12 Dec 201615 Dec 2016

    Publication series

    NameIEEE International Conference on Data Mining Workshops, ICDMW
    Volume0
    ISSN (Print)2375-9232
    ISSN (Electronic)2375-9259

    Conference

    Conference16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
    Country/TerritorySpain
    CityBarcelona
    Period12/12/1615/12/16

    Keywords

    • Anomaly Detection
    • Long-Range Dependence
    • Predictive Maintenance
    • Quasi-seasonal trends
    • Software-Intensive Systems

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