Regenerative simulation methods for local area computer networks

Abstract

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Local area computer network simulations are inherently non-Markovian in that the underlying stochastic process cannot be modeled as a Markov chain with countable state space. We restrict attention to local network simulations whose underlying stochastic process can be represented as a generalized semi-Markov process (GSMP). Using new better than used distributional assumptions and sample path properties of the GSMP, we provide a geometric trials criterion for recurrence in this setting. We also provide conditions which ensure that a GSMP is a regenerative process and that the expected time between regeneration points is finite. Steady-state estimation procedures for ring and bus network simulations follow from these results.