A Hierarchical Framework for Modeling and Forecasting Web Server Workload

Proactive management of web server farms requires accurate prediction of workload. An exemplary measure of workload is the amount of service requests per unit time. As a time series, the workload exhibits not only short-term random fluctuations but also prominent periodic (daily) patterns that evolve randomly from one period to another. A hierarchical framework with multiple time scales is proposed in this paper to model such time series. It leads to an adaptive procedure that provides both long-term (in days) and short-term (in minutes) predictions with simultaneous confidence (prediction) bands which accommodate not only serial correlation but also heavy-tailedness, heteroscedasticity, and nonstationarity of the data.

By: Ta-Hsin Li

Published in: RC22958 in 2003


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 .