Improving Service Diagnosis through Increased Monitoring Granularity

Due to their loose coupling and highly dynamic nature, service-oriented systems offer many benefits for realizing fault tolerance and supporting trustworthy computing. They enable automatic system reconfiguration in case that a faulty service is detected. Spectrum-based fault localization (SFL) is a statistics-based diagnosis technique that can effectively be applied to pinpoint problematic services. It works by monitoring service usage in system transactions and comparing service coverage with pass/fail observations. SFL exhibits poor performance in diagnosing faulty services in cases when services are tightly coupled. In this paper, we study how and to which extent an increase in monitoring granularity can help to improve correct diagnosis of tightly coupled faulty services. We apply SFL in a real service-based system, for which we show that 100% correct identification of faulty services can be achieved through an increase in the monitoring granularity.