Abstract
AbstractThe article summarises author’s experience in two problems related to the use of queueing models in performance evaluation of computer networks: modelling transient states of queues and computations for queueing network models having large number of nodes. Both issues are not well represented in classical queueing theory, yet important to applications, because the observed traffic is time dependant and network topologies that should be considered become larger and larger. The article discusses two approaches: diffusion approximation and fluid-flow approximation that can cope with much larger models that are attainable with the use of Markov chains.KeywordsFluid Flow ApproximationDiffusion ApproximationDiscard ProbabilityInstantaneous Queue LengthSteady-state Markov ChainThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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