A Survey of Coflow Scheduling Schemes for Data Center Networks
A Survey of Coflow Scheduling Schemes for Data Center Networks
复制标题
数据中心网络Coflow调度方案综述
DOI：
10.1109/mcom.2017.1700267
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发表时间：
2018-01
影响因子：
11.2
通讯作者：
Yunjie Liu
中科院分区：
文献类型：
--
作者：
Shuo Wang;Jiao Zhang;Tao Huang;Jiang Liu;Tian Pan;Yunjie Liu
MapReduce、Spark等集群计算应用已广泛部署在数据中心，以支持商业应用和科学研究。这些应用程序通常涉及两组机器生成的并行流的集合，最慢的流将决定应用程序的完成。然而，现有的网络级优化与应用程序的特殊通信模式无关。他们中的大多数只专注于提高单个流程的完成时间，而不是并行流程的集合。最近提出的协流抽象准确地表达了集群计算应用的需求，并为减少集群计算中作业的完成时间创造了新的机会。由于协流特性的巨大差异，协流调度在数据中心网络中面临着巨大的挑战。已经提出了许多工作来解决各种挑战中的一个或一些。因此，在本文中，我们调查了数据中心网络协流调度的最新发展。我们希望本文能够帮助读者快速了解每个问题的原因并了解当前的研究进展，从而为该领域进一步的研究提供指导和动力。
Cluster computing applications, such as MapReduce and Spark, have been widely deployed in data centers to support commercial applications and scientific research. These applications often involve a collection of parallel flows generated by two groups of machines, and the slowest flow will determine the completion of applications. However, existing network-level optimizations are agnostic to the special communication pattern of the applications. Most of them only focus on improving the completion time of an individual flow instead of a collection of parallel flows. The recently proposed coflow abstraction exactly expresses the requirements of cluster computing applications and creates new opportunities to reduce the completion time of jobs in cluster computing. Due to the wide variations in coflow characteristics, coflow scheduling faces great challenges in data center networks. Much work has been proposed to solve one or some of the various challenges. Therefore, in this article, we survey the latest development in coflow scheduling for data center networks. We hope that this article will help readers quickly understand the causes of each problem and learn about current research progress, so as to serve as a guide and motivation toward further research in this area.
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影响因子：
35.6
作者：
R. Rojas-Cessa;Yagiz Kaymak;Z. Dong
通讯作者：
R. Rojas-Cessa;Yagiz Kaymak;Z. Dong
实现数据中心网络实用且近乎最优的协流调度
DOI：
10.1109/tpds.2016.2525767
发表时间：
2016-11-01
影响因子：
5.3
作者：
Luo, Shouxi;Yu, Hongfang;Li, Lemin
通讯作者：
Li, Lemin
影响因子：
2.8
作者：
Chowdhury, Mosharaf;Zhong, Yuan;Stoica, Ion
通讯作者：
Stoica, Ion
DOI：
10.1145/2934872.2934880
发表时间：
2016-08
期刊：
Proceedings of the 2016 ACM SIGCOMM Conference
影响因子：
--
作者：
Hong Zhang;Li Chen;Bairen Yi;Kai Chen;Mosharaf Chowdhury;Yanhui Geng
通讯作者：
Hong Zhang;Li Chen;Bairen Yi;Kai Chen;Mosharaf Chowdhury;Yanhui Geng
DOI：
10.1109/infocom.2015.7218408
发表时间：
2015-08
期刊：
2015 IEEE Conference on Computer Communications (INFOCOM)
影响因子：
--
作者：
Yangming Zhao;Kai Chen;Wei Bai;Minlan Yu;Chen Tian;Yanhui Geng;Yiming Zhang;Dan Li;Sheng Wang-S
通讯作者：
Yangming Zhao;Kai Chen;Wei Bai;Minlan Yu;Chen Tian;Yanhui Geng;Yiming Zhang;Dan Li;Sheng Wang-S