A Hierarchical Framework for Modeling and Forecasting Web Server Workload

Abstract

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 to model such time series. This framework leads to an adaptive procedure that provides both long-term (in days) and short-term (in minutes) predictions with simultaneous confidence bands that accommodate not only serial correlation, but also heavy tailedness, heteroscedasticity, and nonstationarity of the data.