Fast Extraction of Adaptive Change Point Based Patterns for Problem Resolution in Enterprise Systems

Enterprise middleware systems typically consist of a large cluster of machines with stringent performance requirements. Hence, when a performance problem occurs in such environments, it is critical that the health monitoring software identifies the root cause with minimal delay. A technique commonly used for isolating root causes is rule definition, which involves specifying combinations of events that cause particular problems. However, such predefined rules (or problem signatures) tend to be inflexible, and crucially depend on domain experts for their definition. We present in this paper a method that automatically generates change point based problem signatures using administrator feedback, thereby removing the dependence on domain experts. The problem signatures generated by our method are flexible, in that they do not require exact matches for triggering, and adapt as more information becomes available. Unlike traditional data mining techniques, where one requires a large number of problem instances to extract meaningful patterns, our method requires few fault instances to learn problem signatures. We demonstrate the efficacy of our approach by learning problem signatures for five common problems that occur in enterprise systems and reliably recognizing these problems with a small number of learning instances.