A Clustering Approximation Technique for Queueing Network Models with a Large Number of Chains

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Abstract
The past few years have witnessed an increasing number of large distributed computer system implementations based on local area networks. In these systems a number of resources (CPU's, file servers, disks, etc.) are shared among jobs originating at different sites. Evaluating the performance of such large systems typically requires the solution of a queueing network model with a large number of closed chains, which precludes the use of exact solution techniques. Therefore, it is important to develop accurate and cost-effective methods for the approximate analysis of closed queueing networks with many chains. In this paper, we present an approach based on the clustering of chains and service centers. The method is applicable to queueing networks with single server fixed rate, infimite server and multiple server service centers. We present the results obtained when the method is used to solve large queueing network models. Extensive comparison of this method to existing approximation techniques indicates that the approach has better accuracy/cost characteristics.

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