Statistical methods for locating performance problems in multi-tier applications

Abstract

This thesis describes an algorithm developed to aid in solving the problem of performance diagnosis, by automatically identifying the specific component in a multicomponent application system responsible for a performance problem. The algorithm monitors the system, collecting load and latency information from each component, searches the data for patterns indicative of performance saturation using statistical methods, and uses a machine learning classifier to interpret those results. The algorithm was tested with two test applications in several configurations, with different performance problems synthetically introduced. The algorithm correctly located these problems as much as 90% of the time, indicating that this is a good approach to the problem of automatic performance problem location. Also, the experimentation demonstrated that the algorithm can locate performance problems in environments different from those for which it was designed and from that on which it was trained.