{"title":"Multiple constraints QoS routing using priority metrics with control variables","authors":"Maneenate Puongmanee, T. Sanguankotchakorn","doi":"10.1109/ICON.2013.6781973","DOIUrl":null,"url":null,"abstract":"To ensure the performance of Multi-Constrained Path (MCP) problem with respect to Quality of Service (QoS) requirements is NP-complete problem. The algorithm has to find the complete path from source to destination satisfying more than one constraint. This research aims to improve the performance of existing algorithm by assigning the appropriate priority to each link weight component. We use two main concepts which are nonlinear cost function and look-ahead concept in our modified algorithm. In the simulation, we use five different networks with two link-weight scenarios generated randomly from uniform and normal distributions. Then, we compare the results of success ratio (SR) and computational complexity as the performance measure. We found that our algorithm always gives a better performance in terms of SR than H_MCOP, but lesser performance in terms of computational complexity.","PeriodicalId":219583,"journal":{"name":"2013 19th IEEE International Conference on Networks (ICON)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 19th IEEE International Conference on Networks (ICON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICON.2013.6781973","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
To ensure the performance of Multi-Constrained Path (MCP) problem with respect to Quality of Service (QoS) requirements is NP-complete problem. The algorithm has to find the complete path from source to destination satisfying more than one constraint. This research aims to improve the performance of existing algorithm by assigning the appropriate priority to each link weight component. We use two main concepts which are nonlinear cost function and look-ahead concept in our modified algorithm. In the simulation, we use five different networks with two link-weight scenarios generated randomly from uniform and normal distributions. Then, we compare the results of success ratio (SR) and computational complexity as the performance measure. We found that our algorithm always gives a better performance in terms of SR than H_MCOP, but lesser performance in terms of computational complexity.