Towards the improvement of performance anomaly prediction

Marat Zhanikeev, Yoshiaki Tanaka

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Growing demand for pro-active abilities in network management requires performance monitoring agents not only to be able to monitor the anomalies, but also to predict future occurrences. Recent research in this area would usually apply a neural network algorithm on raw SNMP or NetFlow data to obtain the knowledge about the patterns in performance data. The results are not always satisfactory due to highly unpredictable nature of cross-traffic in the network. This paper attempts to improve the prediction quality by using data obtained from end-to-end probing. The results prove higher resilience to cross-traffic interference and better pattern recognition.

    Original languageEnglish
    Title of host publicationFirst IEEE and IFIP International Conference in Central Asia on Internet, 2005
    Volume2005
    DOIs
    Publication statusPublished - 2005
    EventFirst IEEE and IFIP International Conference in Central Asia on Internet, 2005 - Bishkek
    Duration: 2005 Sep 262005 Sep 28

    Other

    OtherFirst IEEE and IFIP International Conference in Central Asia on Internet, 2005
    CityBishkek
    Period05/9/2605/9/28

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    ASJC Scopus subject areas

    • Engineering(all)

    Cite this

    Zhanikeev, M., & Tanaka, Y. (2005). Towards the improvement of performance anomaly prediction. In First IEEE and IFIP International Conference in Central Asia on Internet, 2005 (Vol. 2005). [1598205] https://doi.org/10.1109/CANET.2005.1598205