An empirical investigation of Web session workloads: Can self-similarity be explained by deterministic chaos?

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• 作者：Scott Dick ; Omolbanin Yazdanbaksh ; Xiuli Tang ; Toan Huynh ; James Miller
• 关键词：Web traffic ; Session workload ; Traffic modeling ; Chaos theory ; Nonlinear time series analysis
• 刊名：Information Processing and Management
• 出版年：January, 2014
• 年：2014
• 卷：50
• 期：1
• 页码：41-53
• 全文大小：1082 K

文摘

Several studies of Web server workloads have hypothesized that these workloads are self-similar. The explanation commonly advanced for this phenomenon is that the distribution of Web server requests may be heavy-tailed. However, there is another possible explanation: self-similarity can also arise from deterministic, chaotic processes. To our knowledge, this possibility has not previously been investigated, and so existing studies on Web workloads lack an adequate comparison against this alternative. We conduct an empirical study of workloads from two different Web sites: one public university, and one private company, using the largest datasets that have been described in the literature. Our study employs methods from nonlinear time series analysis to search for chaotic behavior in the web logs of these two sites. While we do find that the deterministic components (i.e. the well-known 鈥渨eekend effect鈥? are significant components in these time series, we do not find evidence of chaotic behavior. Predictive modeling experiments contrasting heavy-tailed with deterministic models showed that both approaches were equally effective in modeling our datasets.