Institutional Repository
| Workload-aware anomaly detection for web applications | |
| Wang, Tao (1); Wei, Jun (1); Zhang, Wenbo (2); Zhong, Hua (2); Huang, Tao (1); Wang, T.(wangtao08@otcaix.iscas.ac.cn) | |
| 2014 | |
| Source | Journal of Systems and Software
 |
| ISSN | 1641212 |
| Volume | 89Issue:1Pages:19-32 |
| English Abstract | The failure of Web applications often affects a large population of customers, and leads to severe economic loss. Anomaly detection is essential for improving the reliability of Web applications. Current approaches model correlations among metrics, and detect anomalies when the correlations are broken. However, dynamic workloads cause the metric correlations to change over time. Moreover, modeling various metric correlations are difficult in complex Web applications. This paper addresses these problems and proposes an online anomaly detection approach for Web applications. We present an incremental clustering algorithm for training workload patterns online, and employ the local outlier factor (LOF) in the recognized workload pattern to detect anomalies. In addition, we locate the anomalous metrics with the Student's t-test method. We evaluated our approach on a testbed running the TPC-W industry-standard benchmark. The experimental results show that our approach is able to (1) capture workload fluctuations accurately, (2) detect typical faults effectively and (3) has advantages over two contemporary ones in accuracy. © 2013 Elsevier Inc.; The failure of Web applications often affects a large population of customers, and leads to severe economic loss. Anomaly detection is essential for improving the reliability of Web applications. Current approaches model correlations among metrics, and detect anomalies when the correlations are broken. However, dynamic workloads cause the metric correlations to change over time. Moreover, modeling various metric correlations are difficult in complex Web applications. This paper addresses these problems and proposes an online anomaly detection approach for Web applications. We present an incremental clustering algorithm for training workload patterns online, and employ the local outlier factor (LOF) in the recognized workload pattern to detect anomalies. In addition, we locate the anomalous metrics with the Student's t-test method. We evaluated our approach on a testbed running the TPC-W industry-standard benchmark. The experimental results show that our approach is able to (1) capture workload fluctuations accurately, (2) detect typical faults effectively and (3) has advantages over two contemporary ones in accuracy. © 2013 Elsevier Inc. |
| Indexed Type | SCI ; EI |
| Keyword | Anomaly Detection Web Applications Local Outlier Factor |
| Department | (1) State Key Laboratory of Computer Science, Beijing 100190 PR, China; (2) Institute of Software, Chinese Academy of Sciences, Beijing 100190, PR, China; (3) University of Chinese Academy of Sciences, Beijing 100049 PR, China |
| Language | 英语 |
| WOS ID | WOS:000331432600003 |
| Citation statistics | |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16869 |
| Collection | 中国科学院软件研究所 |
| Corresponding Author | Wang, T.(wangtao08@otcaix.iscas.ac.cn) |
| Recommended Citation GB/T 7714 | Wang, Tao ,Wei, Jun ,Zhang, Wenbo ,et al. Workload-aware anomaly detection for web applications[J]. Journal of Systems and Software,2014,89(1):19-32. |
| APA | Wang, Tao ,Wei, Jun ,Zhang, Wenbo ,Zhong, Hua ,Huang, Tao ,&Wang, T..(2014).Workload-aware anomaly detection for web applications.Journal of Systems and Software,89(1),19-32. |
| MLA | Wang, Tao ,et al."Workload-aware anomaly detection for web applications".Journal of Systems and Software 89.1(2014):19-32. |
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