Web server performance analysis using histogram workload models

Web servers are required to perform millions of transaction requests per day at an acceptable Quality of Service (QoS) level in terms of client response time and server throughput. Consequently, a thorough understanding of the performance capabilities and limitations of web servers is critical. Finding a simple web traffic model described by a reasonable number of parameters that enables powerful analysis methods and provides accurate results has been a challenging problem during the last few decades.This paper proposes a discrete statistical description of web traffic that is based on histograms. In order to reflect the second-order statistics (long-range dependence and self-similarity) of the workload, this basic model has been extended using the Hurst parameter. Then, a system performance model-based on histogram operators (histogram calculus) is introduced.The proposed model has been evaluated using real workload traces using a single-site server model. These evaluations show that the model is accurate and improves the results of classic queueing models. The model provides an excellent basis for a decision support tool to predict the behavior of web servers.