Detecting and diagnosing application misbehaviors in ‘on-demand’ virtual computing infrastructures

Numerous automated anomaly detection and application performance modeling and management tools are available to detect and diagnose faulty application behavior. However, these tools have limited utility in ‘on-demand’ virtual computing infrastructures because of the increased tendencies for the applications in virtual machines to migrate across un-comparable hosts in virtualized environments and the unusually long latency associated with the training phase. The relocation of the application subsequent to the training phase renders the already collected data meaningless and the tools need to re-initiate the learning process on the new host afresh. Further, data on several metrics need to be correlated and analyzed in real time to infer application behavior. The multivariate nature of this problem makes detection and diagnosis of faults in real time all the more challenging as any suggested approach must be scalable. In this paper, we provide an overview of a system architecture for detecting and diagnosing anomalous application behaviors even as applications migrate from one host to another and discuss a scalable approach based on Hotelling's T2 statistic and MYT decomposition. We show that unlike existing methods, the computations in the proposed fault detection and diagnosis method is parallelizable and hence scalable.