M. Billal, M. Arani, Mohsen Momenitabar, Hamzeh Davarikia
{"title":"Improving Stochastic and Dynamic Communication Networks by Optimizing Throughput","authors":"M. Billal, M. Arani, Mohsen Momenitabar, Hamzeh Davarikia","doi":"10.1109/DASA54658.2022.9765036","DOIUrl":null,"url":null,"abstract":"This study measures a communication network's performance by constructing models for the expected value. A comprehensive stochastic network model whose nodes and arcs are subject to failure is proposed in this study. We can monitor existing network performance and forecast the performance of new or improved networks. For the purpose of this paper, dynamic network flow models with a stochastic network representation of a communication network are used. The approach uses these performance measure models to produce bounds for the expected values of the vector time-series performance data that can be the output of network monitoring.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DASA54658.2022.9765036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This study measures a communication network's performance by constructing models for the expected value. A comprehensive stochastic network model whose nodes and arcs are subject to failure is proposed in this study. We can monitor existing network performance and forecast the performance of new or improved networks. For the purpose of this paper, dynamic network flow models with a stochastic network representation of a communication network are used. The approach uses these performance measure models to produce bounds for the expected values of the vector time-series performance data that can be the output of network monitoring.