A hierarchical framework for modeling and forecasting web server workload

• 成果类型: Article
• 署名作者: Li, TH
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214505000000565
• 发表日期: 2005
• 页码: 748-763
• 关键词: selection

摘要

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.