{"title":"RED与REM的仿真比较","authors":"S. Athuraliya, S. Low","doi":"10.1109/ICON.2000.875770","DOIUrl":null,"url":null,"abstract":"We proposed earlier (Athuraliya et al. 2000) an optimization based flow control for the Internet called random exponential marking (REM). REM consists of a link algorithm, that probabilistically marks packets inside the network, and a source algorithm, that adapts source rate to observed marking. The marking probability is exponential in a link congestion measure, so that the end-to-end marking probability is exponential in a path congestion measure. Because of the finer measure of congestion provided by REM, sources do not constantly probe the network for spare capacity, but settle around a globally optimal equilibrium, thus avoiding the perpetual cycle of sinking into and recovering from congestion. In this paper we compare the performance of REM with Reno over RED (random early detection) through simulation.","PeriodicalId":191244,"journal":{"name":"Proceedings IEEE International Conference on Networks 2000 (ICON 2000). Networking Trends and Challenges in the New Millennium","volume":"85 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Simulation comparison of RED and REM\",\"authors\":\"S. Athuraliya, S. Low\",\"doi\":\"10.1109/ICON.2000.875770\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We proposed earlier (Athuraliya et al. 2000) an optimization based flow control for the Internet called random exponential marking (REM). REM consists of a link algorithm, that probabilistically marks packets inside the network, and a source algorithm, that adapts source rate to observed marking. The marking probability is exponential in a link congestion measure, so that the end-to-end marking probability is exponential in a path congestion measure. Because of the finer measure of congestion provided by REM, sources do not constantly probe the network for spare capacity, but settle around a globally optimal equilibrium, thus avoiding the perpetual cycle of sinking into and recovering from congestion. In this paper we compare the performance of REM with Reno over RED (random early detection) through simulation.\",\"PeriodicalId\":191244,\"journal\":{\"name\":\"Proceedings IEEE International Conference on Networks 2000 (ICON 2000). Networking Trends and Challenges in the New Millennium\",\"volume\":\"85 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings IEEE International Conference on Networks 2000 (ICON 2000). Networking Trends and Challenges in the New Millennium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICON.2000.875770\",\"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 IEEE International Conference on Networks 2000 (ICON 2000). Networking Trends and Challenges in the New Millennium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICON.2000.875770","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
摘要
我们早些时候(Athuraliya et al. 2000)提出了一种基于优化的互联网流量控制,称为随机指数标记(REM)。REM由链路算法和源算法组成,链路算法对网络内的数据包进行概率标记,源算法根据观察到的标记调整源速率。在链路拥塞度量中,标记概率是指数的,因此在路径拥塞度量中,端到端标记概率是指数的。由于REM提供了更精细的拥塞度量,资源不会不断地探测网络是否有空闲容量,而是围绕全局最优均衡解决,从而避免了陷入拥塞和从拥塞中恢复的永久循环。本文通过仿真比较了REM和Reno在RED(随机早期检测)上的性能。
We proposed earlier (Athuraliya et al. 2000) an optimization based flow control for the Internet called random exponential marking (REM). REM consists of a link algorithm, that probabilistically marks packets inside the network, and a source algorithm, that adapts source rate to observed marking. The marking probability is exponential in a link congestion measure, so that the end-to-end marking probability is exponential in a path congestion measure. Because of the finer measure of congestion provided by REM, sources do not constantly probe the network for spare capacity, but settle around a globally optimal equilibrium, thus avoiding the perpetual cycle of sinking into and recovering from congestion. In this paper we compare the performance of REM with Reno over RED (random early detection) through simulation.