Exploiting Context-Aware Event Data for Fault Analysis

Fault analysis in communication networks and distributed systems is a difficult process that heavily depends on system administrator’s experience and supporting tools. This process usually requires analytic techniques and several types of event data including log events, debug messages, trace obtained from these systems to investigate the root cause of faults. This paper introduces an approach of exploiting context-aware data and classification technique for improving this process. This approach uses both event data and context-aware data including CPU load, memory, processes, temperature, status to train a decision tree, and then applies the tree to assess suspected events. We have implemented and experimented the approach on the OpenStack cloud computing system with the Hadoop computing service and MELA event collection system. The experimental results reveal that the accuracy score of the approach reaches 85% on average. The paper also includes detailed analysis for the results