Gianfranco Balbo, Andrea Marin, Diletta Olliaro, Matteo Sereno
{"title":"Computational Algorithms for the Product Form Solution of Closed Queuing Networks with Finite Buffers and Skip-Over Policy","authors":"Gianfranco Balbo, Andrea Marin, Diletta Olliaro, Matteo Sereno","doi":"arxiv-2409.08075","DOIUrl":null,"url":null,"abstract":"Closed queuing networks with finite capacity buffers and skip-over policies\nare fundamental models in the performance evaluation of computer and\ncommunication systems. This technical report presents the details of\ncomputational algorithms to derive the key performance metrics for such\nnetworks. The primary focus is on the efficient computation of the\nnormalization constant, which is critical for determining the steady-state\nprobabilities of the network states under investigation. A convolution\nalgorithm is proposed, which paves the way for the computation of key\nperformance indices, such as queue length distribution and throughput,\naccommodating the intricacies introduced by finite capacity constraints and\nskip-over mechanisms. Finally, an extension of the traditional Mean Value\nAnalysis algorithm addressing numerical stability is provided. The approaches\ndiscussed here allow make the investigation of large-scale networks feasible\nand enable the development of robust implementations of these techniques for\npractical use.","PeriodicalId":501291,"journal":{"name":"arXiv - CS - Performance","volume":"84 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Performance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.08075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Closed queuing networks with finite capacity buffers and skip-over policies
are fundamental models in the performance evaluation of computer and
communication systems. This technical report presents the details of
computational algorithms to derive the key performance metrics for such
networks. The primary focus is on the efficient computation of the
normalization constant, which is critical for determining the steady-state
probabilities of the network states under investigation. A convolution
algorithm is proposed, which paves the way for the computation of key
performance indices, such as queue length distribution and throughput,
accommodating the intricacies introduced by finite capacity constraints and
skip-over mechanisms. Finally, an extension of the traditional Mean Value
Analysis algorithm addressing numerical stability is provided. The approaches
discussed here allow make the investigation of large-scale networks feasible
and enable the development of robust implementations of these techniques for
practical use.