Detecting anomaly in cloud platforms using a wavelet-based framework

Cloud computing enables the delivery of compute resources as services in an on-demand fashion. The reliability of these services is of significant importance to their consumers. The presence of anomaly in Cloud platforms can put their reliability into question, since an anomaly indicates deviation from normal behaviour. Monitoring enables efficient Cloud service provisioning management; however, most of the management efforts are focused on the performance of the services and little attention is paid to detecting anomalous behaviour from the gathered monitoring data. In addition, the existing solutions for detecting anomaly in Clouds lacks a multi-dimensional approach. In this chapter, we present a wavelet-based anomaly detection framework that is capable of analysing multiple monitored metrics simultaneously to detect anomalous behaviour. It operates in both frequency and time domains in analysing monitoring data that represents system behaviour. The framework is first trained using over seven days worth of historical monitoring data to identify healthy behaviour. Based on this training, anomalous behaviour can be detected as deviations from the healthy system. The effectiveness of the proposed framework was evaluated based on a Cloud service deployment use-case scenario that produced both healthy and anomalous behaviour.