{"title":"多业务网络的资源优化分配","authors":"R. Simon, B. Jukic, Woan Sun Chang","doi":"10.1109/SIMSYM.2000.844898","DOIUrl":null,"url":null,"abstract":"The next generation of communication networks will simultaneously offer multiple service classes capable of supporting both real-time and best-effort traffic. At the level of an individual network router or gateway, a critical question that needs to be addressed is the development of a set of policies and metrics to determine how router resources should be shared between different service classes. We describe a solution to this problem that combines pricing policies with admission control for real-time traffic. We present a measurement-based approach for adaptive pricing that results in near-optimal resource allocation policies between real-time and best-effort traffic. We also show, through simulation, an application of our approach that maximizes overall user value.","PeriodicalId":361153,"journal":{"name":"Proceedings 33rd Annual Simulation Symposium (SS 2000)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Optimal resource allocation for multi-service networks\",\"authors\":\"R. Simon, B. Jukic, Woan Sun Chang\",\"doi\":\"10.1109/SIMSYM.2000.844898\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The next generation of communication networks will simultaneously offer multiple service classes capable of supporting both real-time and best-effort traffic. At the level of an individual network router or gateway, a critical question that needs to be addressed is the development of a set of policies and metrics to determine how router resources should be shared between different service classes. We describe a solution to this problem that combines pricing policies with admission control for real-time traffic. We present a measurement-based approach for adaptive pricing that results in near-optimal resource allocation policies between real-time and best-effort traffic. We also show, through simulation, an application of our approach that maximizes overall user value.\",\"PeriodicalId\":361153,\"journal\":{\"name\":\"Proceedings 33rd Annual Simulation Symposium (SS 2000)\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 33rd Annual Simulation Symposium (SS 2000)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIMSYM.2000.844898\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 33rd Annual Simulation Symposium (SS 2000)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIMSYM.2000.844898","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal resource allocation for multi-service networks
The next generation of communication networks will simultaneously offer multiple service classes capable of supporting both real-time and best-effort traffic. At the level of an individual network router or gateway, a critical question that needs to be addressed is the development of a set of policies and metrics to determine how router resources should be shared between different service classes. We describe a solution to this problem that combines pricing policies with admission control for real-time traffic. We present a measurement-based approach for adaptive pricing that results in near-optimal resource allocation policies between real-time and best-effort traffic. We also show, through simulation, an application of our approach that maximizes overall user value.