A new recursive algorithm for computing generating functions in closed multi-class queueing networks

P. Harrison, Ting Ting Lee
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引用次数: 6

Abstract

We obtain an algorithm that implements a recursive generating function (RGF) for computing the normalising constant in closed, multi-class, product-form queueing networks with multiple, load-independent servers of the same load. It expresses the generating function of a q-class network in terms of the generating functions of a set of (q-1)-class networks. The result for a multi-class network can therefore be deduced hierarchically by finding the normalising constants of a collection of single class networks. A storage management scheme is devised, based on a depth-first recursion tree traversal, to optimise both time and storage requirements and the numerical precision of the resulting RGF algorithm is investigated. In two-class networks, the space and time requirements of RGF are shown to be smaller than for the convolution and RECAL algorithms when the networks contain a moderate to large number of customers. With more classes, RGF gives better performance than the other two methods in many-node networks that are organised in a few groups of several identical nodes.
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一种新的多类封闭排队网络生成函数的递归计算算法
我们获得了一种实现递归生成函数(RGF)的算法,用于计算具有相同负载的多个负载独立服务器的封闭,多类,产品形式排队网络中的规范化常数。它用一组(q-1)类网络的生成函数来表示q类网络的生成函数。因此,多类网络的结果可以通过寻找单类网络集合的归一化常数分层地推导出来。设计了一种基于深度优先递归树遍历的存储管理方案,以优化时间和存储需求,并研究了所得到的RGF算法的数值精度。在两类网络中,当网络包含中等到大量的客户时,RGF的空间和时间需求比卷积和RECAL算法要小。有了更多的类,RGF在多节点网络中提供了比其他两种方法更好的性能,这些网络被组织在几个相同节点的几组中。
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