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Title:
workload-aware online anomaly detection in enterprise applications with local outlier factor
Author: Wang Tao ; Zhang Wenbo ; Wei Jun ; Zhong Hua
Source: Proceedings - International Computer Software and Applications Conference
Conference Name: 36th IEEE Annual International Computer Software and Applications Conference, COMPSAC 2012
Conference Date: July 16, 2012 - July 20, 2012
Issued Date: 2012
Conference Place: Izmir, Turkey
Keyword: Industry ; Statistics
Indexed Type: EI
ISSN: 0730-3157
ISBN: 9780769547367
Department: (1) Institute of Software Chinese Academy of Sciences Graduate University Beijing China
Sponsorship: IEEE; IEEE Computer Society
Abstract: Detecting anomalies are essential for improving the reliability of enterprise applications. Current approaches set thresholds for metrics or model correlations between metrics, and anomalies are detected when the thresholds are violated or the correlations are broken. However, we have found that the dynamic workload fluctuating over multiple time scales causes system metrics and their correlations to change. Moreover, it is difficult to model various metric correlations in complex applications. This paper addresses these problems and proposes an online anomaly detection approach for enterprise applications. A method is presented for recognizing workload patterns with an incremental clustering algorithm. The Local Outlier Factor (LOF) based on the specific workload pattern is adopted for detecting anomalies. Our approach is evaluated on a testbed running the TPC-W benchmark. The experimental results show that our approach can capture workload fluctuations accurately and detect the typical faults effectively. © 2012 IEEE.
English Abstract: Detecting anomalies are essential for improving the reliability of enterprise applications. Current approaches set thresholds for metrics or model correlations between metrics, and anomalies are detected when the thresholds are violated or the correlations are broken. However, we have found that the dynamic workload fluctuating over multiple time scales causes system metrics and their correlations to change. Moreover, it is difficult to model various metric correlations in complex applications. This paper addresses these problems and proposes an online anomaly detection approach for enterprise applications. A method is presented for recognizing workload patterns with an incremental clustering algorithm. The Local Outlier Factor (LOF) based on the specific workload pattern is adopted for detecting anomalies. Our approach is evaluated on a testbed running the TPC-W benchmark. The experimental results show that our approach can capture workload fluctuations accurately and detect the typical faults effectively. © 2012 IEEE.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15810
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Wang Tao,Zhang Wenbo,Wei Jun,et al. workload-aware online anomaly detection in enterprise applications with local outlier factor[C]. 见:36th IEEE Annual International Computer Software and Applications Conference, COMPSAC 2012. Izmir, Turkey. July 16, 2012 - July 20, 2012.
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