workload-aware online anomaly detection in enterprise applications with local outlier factor

ISCAS OpenIR

	workload-aware online anomaly detection in enterprise applications with local outlier factor
	Wang Tao; Zhang Wenbo; Wei Jun; Zhong Hua
	2012
会议名称	36th IEEE Annual International Computer Software and Applications Conference, COMPSAC 2012
会议录名称	Proceedings - International Computer Software and Applications Conference
页码	25-34
会议日期	July 16, 2012 - July 20, 2012
会议地点	Izmir, Turkey
收录类别	EI
ISSN	0730-3157
ISBN	9780769547367
部门归属	(1) Institute of Software Chinese Academy of Sciences Graduate University Beijing China
摘要	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.; 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.
关键词	Industry Statistics
主办者	IEEE; IEEE Computer Society
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15810
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Wang Tao,Zhang Wenbo,Wei Jun,et al. workload-aware online anomaly detection in enterprise applications with local outlier factor[C],2012:25-34.