Exploring Time and Frequency Domains for Accurate and Automated Anomaly Detection in Cloud Computing Systems

Cloud computing has become increasingly popular by obviating the need for users to own and maintain complex computing infrastructures. However, due to their inherent complexity and large scale, production cloud computing systems are prone to various runtime problems caused by hardware and software faults and environmental factors. Autonomic anomaly detection is crucial for understanding emergent, cloud-wide phenomena and self-managing cloud resources for system-level dependability assurance. To detect anomalous cloud behaviors, we need to monitor the cloud execution and collect runtime cloud performance data. For different types of failures, the data display different correlations with the performance metrics. In this paper, we present a wavelet-based multi-scale anomaly identification mechanism, that can analyze profiled cloud performance metrics in both time and frequency domains and identify anomalous cloud behaviors. Learning technologies are exploited to adapt the selection of mother wavelets and a sliding detection window is employed to handle cloud dynamicity and improve anomaly detection accuracy. We have implemented a prototype of the anomaly identification system and conducted experiments on an on-campus cloud computing environment. Experimental results show the proposed mechanism can achieve 93.3% detection sensitivity while keeping the false positive rate as low as 6.1% while outperforming other tested anomaly detection schemes.