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A Note On Stable Flow-equivalent Aggregation In Closed Networks

机译:关于封闭网络中稳定的流量等效聚合的注记

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We introduce the Conditional Mean Value Analysis (CMVA) algorithm, an exact solution method for product-form load-dependent closed queueing networks that provides a numerically stable solution of models where the load-dependent Mean Value Analysis (MVA) is numerically unstable. Similarly to the MVA algorithm for constant-rate queues, CMVA performs operations in terms of mean quantities only, i.e., queue-lengths, throughput, response times. Numerical stability derives from a new version of the MVA arrival theorem for load-dependent models which is expressed in terms of mean queue-lengths instead of marginal probabilities. The formula is obtained by the analysis of the conditional state spaces which describe network equilibrium as seen by jobs during their residence times at queues. We also provide a generalization of CMVA to multiclass models that preserves the numerical stability property.
机译:我们介绍了条件均值分析(CMVA)算法,这是一种针对产品形式的依赖于负荷的封闭排队网络的精确求解方法,它为依赖于负荷的均值分析(MVA)在数值上不稳定的模型提供了数值稳定的解决方案。类似于用于恒定速率队列的MVA算法,CMVA仅根据均值即队列长度,吞吐量,响应时间执行操作。数值稳定性源自针对负载相关模型的MVA到达定理的新版本,它以平均队列长度而不是边际概率表示。该公式是通过分析条件状态空间而获得的,该条件状态空间描述了网络在作业在队列中停留期间所看到的网络平衡。我们还提供了CMVA对多类模型的概括,该模型保留了数值稳定性。

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