Toward Latency-Efficient Multicast Coflow Scheduling for Reconfigurable Data Center Networks

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Fulong Li, Fanlong Zhang, Yuhang Wu, Zhuowei Wang, Quan Chen, Yuan Chai, Yongchao Tao
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引用次数: 0

Abstract

The emerging optical circuit technology, which can establish circuit connections among switches, has been proposed as a promising paradigm for data center networks. This paper investigates the problem of minimizing the completion time of multicast coflows in optical circuit switches (OCSs)-based data center networks. The existing works either only focused on multicast coflow scheduling or focused solely on circuit scheduling in OCS-based networks, which greatly limits their performance. Hence, in this paper, we study how to reduce the completion time of multicast coflows by considering circuit scheduling and coflow scheduling simultaneously. First, the problem of multicast coflow scheduling is formulated and proved to be NP-hard. Then, a Delay-efficient Multicast Coflow Scheduling (DMCS) algorithm is proposed by integrating multicast coflow scheduling with circuit scheduling. The proposed DMCS algorithm is proved to have an approximation ratio of at most 2 n $$ 2\sqrt{n} $$ , where n $$ n $$ represents the number of OCS. Through extensive simulations, it is shown that the proposed DMCS algorithm can achieve high performance compared to state-of-the-art methods.

面向可重构数据中心网络的延迟高效多播协同流调度研究
新兴的光电路技术可以在交换机之间建立电路连接,已被提出作为数据中心网络的一个有前途的范例。本文研究了基于光路交换机(ocs)的数据中心网络中组播共流完成时间最小化的问题。现有的研究要么只关注多播协同流调度,要么只关注基于ocs的网络中的电路调度,这极大地限制了它们的性能。因此,本文研究了如何同时考虑电路调度和共流调度来缩短组播共流的完成时间。首先,提出了组播协同流调度问题,并证明了该问题是np困难的。然后,将组播协同流调度与电路调度相结合,提出了一种延迟高效的组播协同流调度算法(DMCS)。证明了所提出的DMCS算法的近似比不超过2 n $$ 2\sqrt{n} $$,其中n $$ n $$表示OCS的个数。通过大量的仿真表明,与现有的方法相比,所提出的DMCS算法可以达到较高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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