Joshua Marker, J. Shea, T. Wong, Eric Graves, Paul L. Yu
{"title":"Identifying Bottleneck Nodes using Packet Delay Statistics","authors":"Joshua Marker, J. Shea, T. Wong, Eric Graves, Paul L. Yu","doi":"10.1109/MILCOM47813.2019.9021045","DOIUrl":null,"url":null,"abstract":"An algorithm to identify the bottleneck nodes linking two component networks in a simple network of networks (NoN) configuration is proposed. The proposed bottleneck identification algorithm is based on applying a support vector machine on clustered packet delay measurements. This algorithm has the advantage that it requires almost no information about the topology of the underlying NoN. Simulation results show that this algorithm can provide very good detection performance when the component networks of the NoN are not too small in size, or when the connectivity between nodes within the component networks is not too sparse.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"233 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MILCOM47813.2019.9021045","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
An algorithm to identify the bottleneck nodes linking two component networks in a simple network of networks (NoN) configuration is proposed. The proposed bottleneck identification algorithm is based on applying a support vector machine on clustered packet delay measurements. This algorithm has the advantage that it requires almost no information about the topology of the underlying NoN. Simulation results show that this algorithm can provide very good detection performance when the component networks of the NoN are not too small in size, or when the connectivity between nodes within the component networks is not too sparse.