Title :
Periodic hidden Markov model-based workload clustering and characterization
Author :
Li, Ning ; Yu, Shun-zheng
Author_Institution :
Dept. of Electron. & Commun. Eng., Sun Yat-Sen Univ., Guangzhou
Abstract :
Workload of a Web server is a complicated stochastic process with non-stationary properties. Userspsila access to Websites is governed by the activities of their daily life. Workload of servers has significant periodicities that reflect daily, weekly and seasonally effect of userspsila access. In this paper, we present a periodic hidden Markov model to characterize the stochastic behavior and the periodicity of workload. Based on this model, we present a method for clustering and classification of workload patterns. Our approach is validated against workloads collected from tens commercial Websites. This approach provides a new solution for traffic modeling and characterization, workload and performance prediction, capacity planning, and statistical anomaly detection of network intrusions.
Keywords :
Web sites; file servers; hidden Markov models; pattern clustering; security of data; Web server; Websites; capacity planning; network intrusions; performance prediction; periodic hidden Markov model; statistical anomaly detection; stochastic process; workload clustering; Capacity planning; Hidden Markov models; Intrusion detection; Navigation; Predictive models; Stochastic processes; Sun; Telecommunication traffic; Traffic control; Web server;
Conference_Titel :
Computer and Information Technology, 2008. CIT 2008. 8th IEEE International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-2357-6
Electronic_ISBN :
978-1-4244-2358-3
DOI :
10.1109/CIT.2008.4594705
