Distributed-optimization-based mix-flow scheduling mechanism for data center networks

doi:10.16511/j.cnki.qhdxxb.2020.21.018

Download: PDF(1541 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract Data center networks are key cloud computing infrastructure whose performance critically impacts the quality of service. Currently, data centers have multiple services with both deadline and non-deadline flows. This paper presents a distributed-optimization-based mix-flow scheduling (DOMS) mechanism to meet the transmission requirements of both types of flows. First, the optimization goals and transmission constraints are defined for both kinds of flows and the mixed-flow scheduling problem is formalized as a real-time rate allocation problem. Then, a coordinated scheduling structure is designed for the hosts and switches that leverages the dual decomposition characteristics of the problem. This method uses a distributed solution method to solve the problem with the flow rates evolving to a global optimal solution. Simulations show that this method effectively reduces deadline miss rates for deadline flows as well as flow completion times for non-deadline flows.

Keywords data center network (DCN) mix-flow scheduling distributed optimization deadline miss rate (DMR) flow completion time (FCT)

Issue Date: 28 April 2021

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	ZHANG Tong
	REN Fengyuan
	SHU Ran

Cite this article:

ZHANG Tong,REN Fengyuan,SHU Ran. Distributed-optimization-based mix-flow scheduling mechanism for data center networks[J]. Journal of Tsinghua University(Science and Technology), 2021, 61(6): 618-625.

URL:

http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2020.21.018 OR http://jst.tsinghuajournals.com/EN/Y2021/V61/I6/618

[1] Cisco. Cisco global cloud index:Forecast and methodology, 2016-2021[R]. San Jose, USA:Cisco, 2018.
[2] NOORMOHAMMADPOUR M, RAGHAVENDRA C S. Datacenter traffic control:Understanding techniques and tradeoffs[J]. IEEE Communications Surveys and Tutorials, 2018, 20(2):1492-1525.
[3] CHEN L, CHEN K, BAI W, et al. Scheduling mix-flows in commodity datacenters with Karuna[C]//Proceedings of the ACM SIGCOMM 2016 Conference. Florianopolis, Brazil:ACM, 2016:174-187.
[4] WILSON C, BALLANI H, KARAGIANNIS T, et al. Better never than late:Meeting deadlines in datacenter networks[C]//Proceedings of the ACM SIGCOMM 2011 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications. Toronto, Canada:ACM, 2011:50-61.
[5] CHOWDHURY M, ZHONG Y, STOICA I. Efficient coflow scheduling with Varys[C]//ACM SIGCOMM 2014 Conference. Chicago, USA:ACM, 2014:443-454.
[6] HONG C Y, CAESAR M, GODFREY P B. Finishing flows quickly with preemptive scheduling[C]//ACM SIGCOMM 2012 Conference. Helsinki, Finland:ACM, 2012:127-138.
[7] ALIZADEH M, YANG S, SHARIF M, et al. pFabric:Minimal near-optimal datacenter transport[C]//ACM SIGCOMM 2013 Conference. Hong Kong, China:ACM, 2013:435-446.
[8] PERRY J, OUSTERHOUT A, BALAKRISHNAN H, et al. Fastpass:A centralized ‘zero-queue’ datacenter network[C]//ACM SIGCOMM 2014 Conference. Chicago, USA:ACM, 2014:307-318.
[9] MUNIR A, BAIG G, IRTEZA S M, et al. Friends, not foes:Synthesizing existing transport strategies for data center networks[C]//ACM SIGCOMM 2014 Conference. Chicago, USA:ACM, 2014:491-502.
[10] BAI W, CHEN L, CHEN K, et al. PIAS:Practical information-agnostic flow scheduling for commodity data centers[J]. IEEE/ACM Transactions on Networking, 2017, 25(4):1954-1967.
[11] GAO P X, NARAYAN A, KUMAR G, et al. pHost:Distributed near-optimal datacenter transport over commodity network fabric[C]//Proceedings of the 11th ACM Conference on Emerging Networking Experiments and Technologies. Heidelberg, Germany:ACM, 2015, 1:1-12.
[12] CHEN L, CHEN K, BAI W, et al. Scheduling mix-flows in commodity datacenters with Karuna[C]//Proceedings of the ACM SIGCOMM 2016 Conference. Florianopolis, Brazil:ACM, 2016:174-187.
[13] WANG T, XU H, LIU F M. Aemon:Information-agnostic mix-flow scheduling in data center networks[C]//Proceedings of the First Asia-Pacific Workshop on Networking. Hong Kong, China:ACM, 2017:106-112.
[14] 臧韦菲, 兰巨龙, 胡宇翔. 基于松弛时间与累计发送量的数据中心网络混合流调度机制[J]. 电子学报, 2019, 47(10):2061-2068.ZANG W F, LAN J L, HU Y X. Slack time and accumulation-based mix-flow scheduling in data center networks[J]. Chinese Journal of Electronics, 2019, 47(10):2061-2068. (in Chinese)
[15] ALIZADEH M, GREENBERG A G, DAVID A. M, et al. Data center TCP (DCTCP)[C]//Proceedings of the ACM SIGCOMM 2010 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications. New Delhi, India:ACM, 2010:63-74.
[16] GREENBERG A, HAMILTON J R, JAIN N, et al. VL2:A scalable and flexible data center network[C]//Proceedings of the ACM SIGCOMM 2009 Conference on Data Communication. Barcelona, Spain:ACM, 2009:51-62.