Wavelet-based approach for anomaly detection of online services in cloud computing systems

doi:10.16511/j.cnki.qhdxxb.2017.22.036

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Abstract As an increasing number of online services have migrated into the cloud, anomaly detection has now become an important problem. Existing efforts detect anomalies by mining real-time workloads; however, the accuracy of such approaches cannot be assured in case of user spikes and application errors. This paper presents a wavelet-based online anomaly detection approach that uses discrete wavelet transforms to decompose real-time workload traces into multiple curves with different frequencies and then applies statistical analysis to the decomposed traces to detect the workload anomalies. Tests show that this approach is more accurate with a lower false-alarm rate than existing approaches.

Keywords cloud computing workload anomaly detection discrete wavelet transform

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TP393

Issue Date: 15 May 2017

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	LIU Jinzhao
	ZHOU Yuezhi
	ZHANG Yaoxue

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LIU Jinzhao,ZHOU Yuezhi,ZHANG Yaoxue. Wavelet-based approach for anomaly detection of online services in cloud computing systems[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(5): 550-554.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2017.22.036 OR http://jst.tsinghuajournals.com/EN/Y2017/V57/I5/550

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