Stochastic Analysis of Deterministic Routing Algorithms in the Presence of Self-Similar Traffic

Abstract

Many performance models for deterministic routing in multicomputer interconnection networks have been derived and analyzed under the assumption of the traditional Poisson stochastic arrival process, which is inherently unable to capture traffic self-similarity revealed by many real-world parallel applications. In an effort towards understanding the network performance under various traffic loads and different design alternatives, this paper presents an analytical model for dimension-ordered routing in k-ary n-cubes when subjected to self-similar traffic. As the service time, blocking probability and waiting time experienced by a message vary from a dimension to another, the design of such a model for dimension-ordered routing poses greater challenges. The developed analytical model is then used to investigate the efficiency of two different ways to organize virtual channels for deterministic routing and to evaluate the impact of self-similar traffic with various Hurst parameters on network performance.