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
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