Zhengwei Zhao, Zhixiong Jiang, Chunyang Lu, Yushan Cai, Jingping Bi
{"title":"数据中心的拥塞控制算法","authors":"Zhengwei Zhao, Zhixiong Jiang, Chunyang Lu, Yushan Cai, Jingping Bi","doi":"10.1109/NAS.2013.19","DOIUrl":null,"url":null,"abstract":"Recently, there has been renewed interest in latency as a primary metric for mainstream data center applications. In this paper, we propose a coordinated mechanism for congestion control and multi-path transmission to reduce flow completion time in data centers. The approach leverages ECN to adaptively react to congestion, prioritizes flows according to their timeliness requirements, and evenly distributes network traffic across multiple paths. Through a real implementation and simulations, we show that our approach can effectively reduce flow completion time under different workflow patterns. Compared to existing solutions based on TCP and flow hashing, our approach achieves an improvement of 30% in application throughput and a reduction of up to 90% in 99th percentile flow completion time for latency-sensitive flows, and a reduction of 50%-80% in average completion time for long background flows.","PeriodicalId":213334,"journal":{"name":"2013 IEEE Eighth International Conference on Networking, Architecture and Storage","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Congestion Control Algorithm for Datacenters\",\"authors\":\"Zhengwei Zhao, Zhixiong Jiang, Chunyang Lu, Yushan Cai, Jingping Bi\",\"doi\":\"10.1109/NAS.2013.19\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recently, there has been renewed interest in latency as a primary metric for mainstream data center applications. In this paper, we propose a coordinated mechanism for congestion control and multi-path transmission to reduce flow completion time in data centers. The approach leverages ECN to adaptively react to congestion, prioritizes flows according to their timeliness requirements, and evenly distributes network traffic across multiple paths. Through a real implementation and simulations, we show that our approach can effectively reduce flow completion time under different workflow patterns. Compared to existing solutions based on TCP and flow hashing, our approach achieves an improvement of 30% in application throughput and a reduction of up to 90% in 99th percentile flow completion time for latency-sensitive flows, and a reduction of 50%-80% in average completion time for long background flows.\",\"PeriodicalId\":213334,\"journal\":{\"name\":\"2013 IEEE Eighth International Conference on Networking, Architecture and Storage\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE Eighth International Conference on Networking, Architecture and Storage\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAS.2013.19\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Eighth International Conference on Networking, Architecture and Storage","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAS.2013.19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recently, there has been renewed interest in latency as a primary metric for mainstream data center applications. In this paper, we propose a coordinated mechanism for congestion control and multi-path transmission to reduce flow completion time in data centers. The approach leverages ECN to adaptively react to congestion, prioritizes flows according to their timeliness requirements, and evenly distributes network traffic across multiple paths. Through a real implementation and simulations, we show that our approach can effectively reduce flow completion time under different workflow patterns. Compared to existing solutions based on TCP and flow hashing, our approach achieves an improvement of 30% in application throughput and a reduction of up to 90% in 99th percentile flow completion time for latency-sensitive flows, and a reduction of 50%-80% in average completion time for long background flows.