Wavelet-based multi-scale anomaly identification in cloud computing systems

Modern cloud computing systems contain thousands of computing and storage servers. Such a scale combined with ever-growing system complexity of their components and interactions, introduces a key challenge to failure and resource management for highly dependable cloud computing. Automated anomaly detection is a crucial technique for understanding emergent, cloud-wide phenomena and self-managing cloud resources for system level dependability assurance. 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 handle cloud dynamicity and improve anomaly detection accuracy. We test a prototype implementation of our cloud anomaly detection mechanism on an institute-wide cloud system. Experimental results show our approach can identify cloud failures accurately.