Ten lectures on wavelets
Wavelets and subband coding
The workload on parallel supercomputers: modeling the characteristics of rigid jobs
Journal of Parallel and Distributed Computing
Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
Long range dependent job arrival process and its implications in grid environments
Proceedings of the first international conference on Networks for grid applications
A multifractal wavelet model with application to network traffic
IEEE Transactions on Information Theory
A Realistic Integrated Model of Parallel System Workloads
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Modelling pilot-job applications on production grids
Euro-Par'09 Proceedings of the 2009 international conference on Parallel processing
Predicting cost amortization for query services
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Towards a profound analysis of bags-of-tasks in parallel systems and their performance impact
Proceedings of the 20th international symposium on High performance distributed computing
A model of pilot-job resource provisioning on production grids
Parallel Computing
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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.