Grid Application Fault Diagnosis Using Wrapper Services and Machine Learning

With increasing size and complexity of Grids manual diagnosis of individual application faults becomes impractical and time-consuming. Quick and accurate identification of the root cause of failures is an important prerequisite for building reliable systems. We describe a pragmatic model-based technique for application-specific fault diagnosis based on indicators, symptoms and rules. Customized wrapper services then apply this knowledge to reason about root causes of failures. In addition to user-provided diagnosis models we show that given a set of past classified fault events it is possible to extract new models through learning that are able to diagnose new faults. We investigated and compared algorithms of supervised classification learning and cluster analysis. Our approach was implemented as part of the Otho Toolkit that 'service-enables' legacy applications based on synthesis of wrapper service.

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