Modeling Job Arrival Process with Long Range Dependence and Burstiness Characteristics

Workload modeling plays a significant role in performance evaluation of large-scale parallel systems such as clusters and grids. It helps to generate synthetic workloads which capture some dominant characteristics of traces (real workloads). Modeling job arrival process is an essential part of workload modeling. Although a job arrival process has many important characteristics such as long range dependence (LRD) and burstiness, most researchers, for simplicity, assume it as a Poisson process in their evaluation work. Furthermore, there is currently almost no research focusing on both LRD and burstiness at the same time according to our investigation. With respect to this research trend, the multifractal wavelet model (MWM) recently has been introduced as a good choice to yield LRD for a job arrival process. Though LRD is well controlled, we observe that a job arrival process produced by MWM does not keep burstiness. In this paper, we present our study on modifying MWM so that not only LRD but also burstiness are kept in the job arrival process. In addition, our modification also fits the marginal distribution better than MWM.