A stable matching based elephant flow scheduling algorithm in data center networks
A stable matching based elephant flow scheduling algorithm in data center networks
复制标题
数据中心网络中基于稳定匹配的大象流调度算法
DOI：
10.1016/j.comnet.2017.04.018
复制
发表时间：
2017-06-19
影响因子：
5.6
通讯作者：
Zhang, Yuan
中科院分区：
文献类型：
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作者：
Zhang, Yuxiang;Cui, Lin;Zhang, Yuan
近年来，随着云计算的发展，数据中心网络已成为工业和学术社区的热门话题。先前的研究表明，通常携带大量数据的大象流对数据中心的效率至关重要。如何有效地安排大象流动成为维持高性能和避免网络拥塞的重要问题。在本文中，我们研究了数据中心的有效流程调度问题，重点是大象流。通过应用稳定的匹配理论，调度问题被建模并被证明是NP-HARD。然后，我们提出了一种有效的方案Fincher，该方案利用软件定义的网络（SDN）来减少延迟并避免数据中心的拥塞。我们已经使用PDX控制器和Mininet实施了Fincher。广泛的评估结果表明，与ECMP和HEDERA相比，Fincher可以将一分解带宽提高30％，并将流量的完成时间平均减少28％。 （c）2017 Elsevier B.V.保留所有权利。
With the development of cloud computing in recent years, data center networks have become a hot topic in both industrial and academic communities. Previous studies have shown that elephant flows, which usually carry large amount of data, are critical to the efficiency of data centers. How to schedule elephant flows efficiently becomes an important issue for maintaining high performance and avoiding network congestion. In this paper, we study the efficient flow scheduling problem in data centers with a focus on elephant flows. By applying stable matching theory, the scheduling problem is modeled and proven to be NP-Hard. Then, we propose Fincher, an efficient scheme leveraging Software-Defined Networking (SDN) to reduce latency and avoid congestions in data centers. We have implemented Fincher with PDX controller and Mininet. Extensive evaluation results demonstrate that Fincher can improve bisection bandwidth by 30% and reduce flow completion time by 28% on average compared to ECMP and Hedera. (C) 2017 Elsevier B.V. All rights reserved.