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Published Articles >> Table of Contents >> Abstract
Sixth IEEE International Symposium on Network Computing and Applications (NCA 2007)
pp. 61-68
Discovering Web Workload Characteristics through Cluster Analysis
1 Li, West Virginia University, USA
1 Goseva-Popstojanova, West Virginia University, USA
1 Ross, West Virginia University, USA
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/NCA.2007.15
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| Abstract |
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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.
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Citation:
1 Li, 1 Goseva-Popstojanova, 1 Ross,
"Discovering Web Workload Characteristics through Cluster Analysis,"
nca,
pp. 61-68,
Sixth IEEE International Symposium on Network Computing and Applications (NCA 2007),
2007
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