A computer system's workload is represented by its multiprogramming level, which is defined as the number of tasks (jobs, customers) which actively compete for resources within the system. In a product-form queuing network model of the system, the workload is modeled by assuming that the multiprogramming level is either fixed (i.e., closed model) or that the multiprogramming level depends upon an outside arrival process (i.e., open model). However, in many actual systems, closed and open models are both inappropriate since the multiprogramming level is neither fixed nor governed by an outside arrival process. In an actual system., the multiprogramming level varies due to features such as task spawning, killing, blocking, parallel processing, and/or simultaneous resource possession. The multiprogramming level is a random variable with an associated distribution. This paper demonstrates that improved models can result from using this multiprogramming level distribution information. Several examples relative to open versus closed models, subsystem models, actual system models, and blocking models are given which demonstrate the applicability of using multiprogramming level distributions. This applicability, shown via the examples, is the main contribution of the paper. The examples also motivate interesting theoretical results relating to open models, closed models, and subsystem models.
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On the applicability of using multiprogramming level distributions
Dowdy, Lawrence W.; Chopra, Manvinder S.
Follow ACM SIGMETRICS Performance Evaluation Review
, Volume 13 (2)
Association for Computing Machinery – Aug 1, 1985
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