{"title":"Notice of Violation of IEEE Publication PrinciplesResource management and quality adaptation in distributed multimedia networks","authors":"A. E. Lawabni, A. Tewfik","doi":"10.1109/ISCC.2005.134","DOIUrl":null,"url":null,"abstract":"In this paper, the problem of allocating multiple finite resources to satisfy the quality of service (QoS) needs of multiple applications along multiple QoS dimensions is presented. A mathematical model that captures the dynamics of such adaptive problem is presented. This model formulates the problem as multiple-choice multidimensional 0-1 knapsack problem (MMKP), an NP-hard optimization problem. A heuristic algorithm is then proposed to solve the MMKP. Experimental results demonstrate that our proposed algorithm finds 96% optimal solutions on average, and outperforms other heuristic algorithms for MMKP. Furthermore, the time required is on average 50% to 70% less than that required by other benchmark heuristics. These two properties make this heuristic a strong candidate for use in real-time applications.","PeriodicalId":315855,"journal":{"name":"10th IEEE Symposium on Computers and Communications (ISCC'05)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"10th IEEE Symposium on Computers and Communications (ISCC'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCC.2005.134","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, the problem of allocating multiple finite resources to satisfy the quality of service (QoS) needs of multiple applications along multiple QoS dimensions is presented. A mathematical model that captures the dynamics of such adaptive problem is presented. This model formulates the problem as multiple-choice multidimensional 0-1 knapsack problem (MMKP), an NP-hard optimization problem. A heuristic algorithm is then proposed to solve the MMKP. Experimental results demonstrate that our proposed algorithm finds 96% optimal solutions on average, and outperforms other heuristic algorithms for MMKP. Furthermore, the time required is on average 50% to 70% less than that required by other benchmark heuristics. These two properties make this heuristic a strong candidate for use in real-time applications.