Performance analysis and capacity planning for e-commerce sites poses an interesting problem: how to best characterize the workload of these sites. Tradition al workload characterization methods, based onhits/set, page views/set, or visits/set, are not appropriate for e-commerce sites. In these environments, customers interact with the site through a series of consecutive and related requests, called sessions. Different navigational patterns can be observed for different groups of customers. In this paper, we propose a methodology for characterizing and generating e-commerce workload models. First, we introduce a state transition graph called Customer Behavior Model Graph (CBMG), that is used to describe the behavior of groups of customers who exhibit similar navigational patterns. A set of useful metrics, analytically derived from the analysis of the CBMG, is presented. Next, we define a workload model and show the steps required to obtain its parameters. We then propose a clustering...
Daniel A. Menascé, Virgilio Almeida, Rodrig