Clustering performance anomalies in web applications based on root causes

Satoshi Iwata, Kenji Kono

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

We propose a performance anomaly clustering method for determining suspicious components in web applications. Our clustering method clusters observed anomalies based on their root causes. The key insight behind our method is that the measurements of anomalies that are negatively affected by the same root cause deviates similarly from standard measurements. The results of case studies, which were conducted using RUBiS [8], an auction prototype modeled after eBay.com [5], are encouraging. From the clustering results, we searched for the components exclusively used by each cluster and successfully determined suspicious components such as server processes, Enterprise Beans, and methods in an Enterprise Bean.

本文言語English
ホスト出版物のタイトルProceedings of the 8th ACM International Conference on Autonomic Computing, ICAC 2011 and Co-located Workshops
ページ221-224
ページ数4
DOI
出版ステータスPublished - 2011
イベント8th ACM International Conference on Autonomic Computing, ICAC 2011 and Co-located Workshops - Karlsruhe, Germany
継続期間: 2011 6 142011 6 18

出版物シリーズ

名前Proceedings of the 8th ACM International Conference on Autonomic Computing, ICAC 2011 and Co-located Workshops

Other

Other8th ACM International Conference on Autonomic Computing, ICAC 2011 and Co-located Workshops
国/地域Germany
CityKarlsruhe
Period11/6/1411/6/18

ASJC Scopus subject areas

  • 計算理論と計算数学
  • 応用数学

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