Giuliano Casale
ABSTRACT
The growing interest in Web applications that satisfy end-to-end Quality of Service (QoS) requirements is leading many organizations to build and analyze performance behavior models. In this direction, Web usage mining techniques may help in the automatic construction of user profiles from Web access logs. However, their use has been mainly limited to customer relationship management (CRM) issues and to market analyses. The aim of this paper is to explain how Web usage mining can be combined with queueing networks for effective Web capacity planning. After introducing a new general relative cosine similarity measure, we define a performance-oriented similarity for Web usage data. A methodology to devise the input parameters of a queueing network from the resulting clusters is also presented. Finally, the proposed approach is illustrated on a simple case study.
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Index Terms
- Combining queueing networks and web usage mining techniques for web performance analysis
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Reviews
Amos O Olagunju
Historical Web site access records are useful for predicting the behaviors of customers and system workload capacity. Constructing representative behavioral profiles of customers from historical Web access logs is not straightforward. Moreover, the reliability of data mining results is contingent on conformity between forecast models and the current behaviors of customers. Mapping profiles of customers to prediction models is crucial in strategic Web capacity planning. Clustering algorithms based on similarity indices for the automatic classification of documents and queries exist in the literature [1]. Casale proposes a novel relative cosine similarity measure for use in system workload performance characterization and Web usage mining algorithms. The newly developed measure produced consistent and reliable similarities for sessions in a synthetic Web data set. Although this relative cosine similarity might be incongruous for comparing heterogeneous system workloads with possibly unreliable data, the paper offers distinctive insights for incorporating Web usage analytical results into queueing network performance models. Online Computing Reviews Service
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