{"title":"Maxmin覆盖组播:速率分配和树结构","authors":"Yi Cui, Yuan Xue, K. Nahrstedt","doi":"10.1109/IWQOS.2004.1309385","DOIUrl":null,"url":null,"abstract":"Although initially proposed as the deployable alternative to IP multicast, overlay multicast actually offers us great flexibilities on QoS-aware resource allocation for network applications. For example, in overlay multicast streaming, (1) the streaming rate of each client can be diversified to better accommodate network heterogeneity, through various end-to-end streaming adaptation techniques; and (2) one can freely organize the overlay session by rearranging the multicast tree, for the purpose of better resource utilization and fairness among all clients. The goal of this paper, is to find the max-min rate allocation in overlay multicast, which is pareto-optimal in terms of network resource utilization, and max-min fair. We approach this goal in two steps. First, we present a distributed algorithm, which is able to return the max-min rate allocation for any given overlay multicast tree. Second, we study the problem of finding the optimal tree, whose max-min rate allocation is optimal among all trees. After proving its NP-hardness, we propose a heuristic algorithm of overlay multicast tree construction. A variation of the heuristic is also designed to handle the dynamic client join/departure. Both of them have approximation bound of 1/2 to the optimal value. Experimental results show that they achieve high average throughput, almost saturate link utilization, and consistent min-favorability.","PeriodicalId":266235,"journal":{"name":"Twelfth IEEE International Workshop on Quality of Service, 2004. IWQOS 2004.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"45","resultStr":"{\"title\":\"Maxmin overlay multicast: rate allocation and tree construction\",\"authors\":\"Yi Cui, Yuan Xue, K. Nahrstedt\",\"doi\":\"10.1109/IWQOS.2004.1309385\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Although initially proposed as the deployable alternative to IP multicast, overlay multicast actually offers us great flexibilities on QoS-aware resource allocation for network applications. For example, in overlay multicast streaming, (1) the streaming rate of each client can be diversified to better accommodate network heterogeneity, through various end-to-end streaming adaptation techniques; and (2) one can freely organize the overlay session by rearranging the multicast tree, for the purpose of better resource utilization and fairness among all clients. The goal of this paper, is to find the max-min rate allocation in overlay multicast, which is pareto-optimal in terms of network resource utilization, and max-min fair. We approach this goal in two steps. First, we present a distributed algorithm, which is able to return the max-min rate allocation for any given overlay multicast tree. Second, we study the problem of finding the optimal tree, whose max-min rate allocation is optimal among all trees. After proving its NP-hardness, we propose a heuristic algorithm of overlay multicast tree construction. A variation of the heuristic is also designed to handle the dynamic client join/departure. Both of them have approximation bound of 1/2 to the optimal value. Experimental results show that they achieve high average throughput, almost saturate link utilization, and consistent min-favorability.\",\"PeriodicalId\":266235,\"journal\":{\"name\":\"Twelfth IEEE International Workshop on Quality of Service, 2004. IWQOS 2004.\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"45\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Twelfth IEEE International Workshop on Quality of Service, 2004. IWQOS 2004.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWQOS.2004.1309385\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Twelfth IEEE International Workshop on Quality of Service, 2004. IWQOS 2004.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWQOS.2004.1309385","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Maxmin overlay multicast: rate allocation and tree construction
Although initially proposed as the deployable alternative to IP multicast, overlay multicast actually offers us great flexibilities on QoS-aware resource allocation for network applications. For example, in overlay multicast streaming, (1) the streaming rate of each client can be diversified to better accommodate network heterogeneity, through various end-to-end streaming adaptation techniques; and (2) one can freely organize the overlay session by rearranging the multicast tree, for the purpose of better resource utilization and fairness among all clients. The goal of this paper, is to find the max-min rate allocation in overlay multicast, which is pareto-optimal in terms of network resource utilization, and max-min fair. We approach this goal in two steps. First, we present a distributed algorithm, which is able to return the max-min rate allocation for any given overlay multicast tree. Second, we study the problem of finding the optimal tree, whose max-min rate allocation is optimal among all trees. After proving its NP-hardness, we propose a heuristic algorithm of overlay multicast tree construction. A variation of the heuristic is also designed to handle the dynamic client join/departure. Both of them have approximation bound of 1/2 to the optimal value. Experimental results show that they achieve high average throughput, almost saturate link utilization, and consistent min-favorability.