{"title":"Brief Announcement: Improved Approximation Algorithms for Scheduling Co-Flows","authors":"S. Khuller, Manish Purohit","doi":"10.1145/2935764.2935809","DOIUrl":null,"url":null,"abstract":"Co-flow scheduling is a recent networking abstraction introduced to capture application-level communication patterns in datacenters. In this paper, we consider the offline co-flow scheduling problem with release times to minimize the total weighted completion time. Recently, Qiu, Stein and Zhong (SPAA, 2015) obtained the first constant approximation algorithms for this problem with a deterministic 67/3-approximation and a randomized (9 + 16√2)/3 ≅ 16.54-approximation. In this paper, we improve upon their algorithm to yield a deterministic 12-approximation algorithm. For the special case when all release times are zero, we obtain a deterministic 8-approximation and a randomized (3+2√2) ≅ 5.83-approximation.","PeriodicalId":346939,"journal":{"name":"Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures","volume":"380 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2935764.2935809","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 28
Abstract
Co-flow scheduling is a recent networking abstraction introduced to capture application-level communication patterns in datacenters. In this paper, we consider the offline co-flow scheduling problem with release times to minimize the total weighted completion time. Recently, Qiu, Stein and Zhong (SPAA, 2015) obtained the first constant approximation algorithms for this problem with a deterministic 67/3-approximation and a randomized (9 + 16√2)/3 ≅ 16.54-approximation. In this paper, we improve upon their algorithm to yield a deterministic 12-approximation algorithm. For the special case when all release times are zero, we obtain a deterministic 8-approximation and a randomized (3+2√2) ≅ 5.83-approximation.