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.
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2017, Vol. 57 
