This paper describes a systematic, statistical approach to characterizing normal system operation for time varying workloads in a web server. We consider the influence 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 workload forecasting, we address the following questions: (1) What will the workload be at a specific time in the future? (2) When will the workload grow beyond a specific limit? (3) When will this limit be exceeded during a specific time-of day or day-of-week? For problem detection, we use the characterization to remove known behavior so as to better detect anomalies when problems are present and to avoid flase alarms when no problem is present.
By: Joseph L. Hellerstein, Fan Zhang, Perwez Shahabuddin
Published in: RC21206 in 1998
LIMITED DISTRIBUTION NOTICE:
This Research Report is available. This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). I have read and understand this notice and am a member of the scientific community outside or inside of IBM seeking a single copy only.
Questions about this service can be mailed to reports@us.ibm.com .