Modeling parallel system workloads with temporal locality

Abstract

In parallel systems, similar jobs tend to arrive within bursty periods. This fact leads to the existence of the locality phenomenon, a persistent similarity between nearby jobs, in real parallel computer workloads. This important phenomenon deserves to be taken into account and used as a characteristic of any workload model. Regrettably, this property has received little if any attention of researchers and synthetic workloads used for performance evaluation to date often do not have locality. With respect to this research trend, Feitelson has suggested a general repetition approach to model locality in synthetic workloads [6]. Using this approach, Li et al. recently introduced a new method for modeling temporal locality in workload attributes such as run time and memory [14]. However, with the assumption that each job in the synthetic workload requires a single processor, the parallelism has not been taken into account in their study. In this paper, we propose a new model for parallel computer workloads based on their result. In our research, we firstly improve their model to control locality of a run time process better and then model the parallelism. The key idea for modeling the parallelism is to control the cross-correlation between the run time and the number of processors. Experimental results show that not only the cross-correlation is controlled well by our model, but also the marginal distribution can be fitted nicely. Furthermore, the locality feature is also obtained in our model. © 2009 Springer-Verlag Berlin Heidelberg.