{"title":"多媒体应用的动态组播路由优化算法","authors":"Moon-sik Kang","doi":"10.1109/MMCS.1997.609573","DOIUrl":null,"url":null,"abstract":"A source-based optimal dynamic multicast routing algorithm is proposed, which satisfies the network conditions of delay constraints and cost minimization and adapts to a dynamic network events. Also, we look at the following network requirements: efficient dynamic group support, high-quality data distribution, and adaptability to dynamically changing events. We construct a dynamic delay-bounded optimal multicast tree using partial multicast routing and evaluate the performance of the proposed algorithm by running simulations, written in C++, with randomly-generated test networks on a Sun Sparc 20 workstation. By choosing appropriate values for the delay bound, we were able to obtain an optimal solution that lies between the minimum-cost solution and the minimum-delay one.","PeriodicalId":302885,"journal":{"name":"Proceedings of IEEE International Conference on Multimedia Computing and Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An optimal dynamic multicast routing algorithm for multimedia applications\",\"authors\":\"Moon-sik Kang\",\"doi\":\"10.1109/MMCS.1997.609573\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A source-based optimal dynamic multicast routing algorithm is proposed, which satisfies the network conditions of delay constraints and cost minimization and adapts to a dynamic network events. Also, we look at the following network requirements: efficient dynamic group support, high-quality data distribution, and adaptability to dynamically changing events. We construct a dynamic delay-bounded optimal multicast tree using partial multicast routing and evaluate the performance of the proposed algorithm by running simulations, written in C++, with randomly-generated test networks on a Sun Sparc 20 workstation. By choosing appropriate values for the delay bound, we were able to obtain an optimal solution that lies between the minimum-cost solution and the minimum-delay one.\",\"PeriodicalId\":302885,\"journal\":{\"name\":\"Proceedings of IEEE International Conference on Multimedia Computing and Systems\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of IEEE International Conference on Multimedia Computing and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMCS.1997.609573\",\"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 of IEEE International Conference on Multimedia Computing and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMCS.1997.609573","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An optimal dynamic multicast routing algorithm for multimedia applications
A source-based optimal dynamic multicast routing algorithm is proposed, which satisfies the network conditions of delay constraints and cost minimization and adapts to a dynamic network events. Also, we look at the following network requirements: efficient dynamic group support, high-quality data distribution, and adaptability to dynamically changing events. We construct a dynamic delay-bounded optimal multicast tree using partial multicast routing and evaluate the performance of the proposed algorithm by running simulations, written in C++, with randomly-generated test networks on a Sun Sparc 20 workstation. By choosing appropriate values for the delay bound, we were able to obtain an optimal solution that lies between the minimum-cost solution and the minimum-delay one.