{"title":"一种新的多类封闭排队网络生成函数的递归计算算法","authors":"P. Harrison, Ting Ting Lee","doi":"10.1109/MASCOT.2004.1348255","DOIUrl":null,"url":null,"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.","PeriodicalId":32394,"journal":{"name":"Performance","volume":"48 1","pages":"231-238"},"PeriodicalIF":0.0000,"publicationDate":"2004-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A new recursive algorithm for computing generating functions in closed multi-class queueing networks\",\"authors\":\"P. Harrison, Ting Ting Lee\",\"doi\":\"10.1109/MASCOT.2004.1348255\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":32394,\"journal\":{\"name\":\"Performance\",\"volume\":\"48 1\",\"pages\":\"231-238\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Performance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MASCOT.2004.1348255\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Performance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MASCOT.2004.1348255","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new recursive algorithm for computing generating functions in closed multi-class queueing networks
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.