{"title":"为容量时变的流提供严格的QoS保证","authors":"P. Swiatek, M. Drwal, A. Grzech","doi":"10.1109/ICSEng.2011.57","DOIUrl":null,"url":null,"abstract":"Many distributed applications require bandwidth provisioning to implement their functionality. A prominent example is a remote real-time monitoring service in e-health system, where a special type of emergency requests require strict guaranties regarding provided transmission rates. We consider the problem of network capacity sharing between two types of flows: standard best-effort flows and QoS-constrained flows. We derive distributed control algorithms for dynamic capacity allocation allowing to serve the QoS-constrained flows by preempting the best-effort flows. Such solution minimizes the amount of unused capacity. We also present how to estimate the capacity needed to deploy QoS-based application in a way to minimize the number of flow preemptions. The presented solution is evaluated in a simulation environment.","PeriodicalId":387483,"journal":{"name":"2011 21st International Conference on Systems Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Providing Strict QoS Guaranties for Flows with Time-varying Capacity Requirements\",\"authors\":\"P. Swiatek, M. Drwal, A. Grzech\",\"doi\":\"10.1109/ICSEng.2011.57\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many distributed applications require bandwidth provisioning to implement their functionality. A prominent example is a remote real-time monitoring service in e-health system, where a special type of emergency requests require strict guaranties regarding provided transmission rates. We consider the problem of network capacity sharing between two types of flows: standard best-effort flows and QoS-constrained flows. We derive distributed control algorithms for dynamic capacity allocation allowing to serve the QoS-constrained flows by preempting the best-effort flows. Such solution minimizes the amount of unused capacity. We also present how to estimate the capacity needed to deploy QoS-based application in a way to minimize the number of flow preemptions. The presented solution is evaluated in a simulation environment.\",\"PeriodicalId\":387483,\"journal\":{\"name\":\"2011 21st International Conference on Systems Engineering\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-08-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 21st International Conference on Systems Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSEng.2011.57\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 21st International Conference on Systems Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSEng.2011.57","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Providing Strict QoS Guaranties for Flows with Time-varying Capacity Requirements
Many distributed applications require bandwidth provisioning to implement their functionality. A prominent example is a remote real-time monitoring service in e-health system, where a special type of emergency requests require strict guaranties regarding provided transmission rates. We consider the problem of network capacity sharing between two types of flows: standard best-effort flows and QoS-constrained flows. We derive distributed control algorithms for dynamic capacity allocation allowing to serve the QoS-constrained flows by preempting the best-effort flows. Such solution minimizes the amount of unused capacity. We also present how to estimate the capacity needed to deploy QoS-based application in a way to minimize the number of flow preemptions. The presented solution is evaluated in a simulation environment.