MMPP Characterization of Web Application Traffic

Abstract

Web application traffic has been shown to exhibit burstiness. The traditional model based on Poisson process is unable to capture the burstiness in traffic. On the other hand, the Markov-modulated Poisson process (MMPP) has been successfully used to model bursty traffic in a variety of computing environments. In this paper, we conduct experiments to investigate the effectiveness of MMPP as a traffic model in the context of resource provisioning in web applications. We first extend an available workload generator to produce a synthetic trace of job arrivals with controlled burstiness. We next consider an existing algorithm, as well as a variant of this algorithm, to fit an MMPP to the synthetic trace; each of them is used to obtain values for the MMPP parameters. The effectiveness of MMPP is then evaluated by comparing the performance results through simulation, using as input the synthetic trace and job arrivals generated by the estimated MMPP.