Characterizing normal operation of a web server: Application to workload forecasting and problem detection

Abstract

This paper describes a systematic, statistical approach to characterizing normal system operation for time varying workloads in a web server. We consider the influences of time-of-day, day-of-week, and month as well as time serial correlations. We apply our approach to two areas of capacity management: workload forecasting and problem detection. For forecasting workloads, we address the following questions: (1) What will the workloads be at a time in the future? (2) When will the workloads grow beyond a specific limit? (3) When will this limit be exceeded for a specific time-of-day or day-of-week week? For problem detection we use the characterization to remove known behavior so as to better detect anomalies when problems are present as well as to avoid false alarms when no problem is present.