Accounting for characteristics of session workloads: A study based on partly-open queue

Many systems, including Web and Software as a Service (SaaS) are best characterized with session-based workloads. Empirical studies have shown that Web session arrivals exhibit long range dependence and that the number of request in a session is well modeled with skewed or heavy-tailed distributions. However, models that account for session workloads characterized by empirically observed phenomena and studies of their impact on performance metrics are lacking. In this paper, we use partly-open queue to account for session-based workloads in a physically meaningful way and use simulation to analyze the behavior of the Web system under Long Range Dependent (LRD) session arrival process and skewed distribution for the number of requests in a session. Our results show that the percentage of dropped sessions, mean queue length, mean waiting time, and the useful server utilization are all affected by the LRD session arrivals and the statistics of the number of requests within a session. The impact is higher in the case of more prominent longrange dependence. Interestingly, both request arrival process and request departure process are long-range dependent, even in the case when session arrivals are Poisson.