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Discovering Web Workload Characteristics through Cluster Analysis

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3 Author(s)
Fengbin Li ; West Virginia Univ., Morgantown ; Goseva-Popstojanova, K. ; Ross, A.

In this paper we present clustering analysis of session-based Web workloads of eight Web servers using the intrasession characteristics (i.e., number of requests per session, session length in time, and bytes transferred per session) as variables. We use K-means algorithm and the Mahalanobis distance, and analyze the heavy-tailed behavior of intra-session characteristics and their correlations for each cluster. Our results show that clustering provides an efficient way to classify tens or hundreds thousands of sessions into several coherent classes that efficiently describe Web workloads. These classes reveal phenomena that cannot be observed when studying the workload as a whole.

Published in:

Network Computing and Applications, 2007. NCA 2007. Sixth IEEE International Symposium on

Date of Conference:

12-14 July 2007