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A Power-Aware Routing Algorithm in Fat-tree Date Center Networks

机译:脂肪树日期中心网络中的动力感知路由算法

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摘要

Power consumption has become a key issue during the last few years and that of Internet in particular, is not negligible. With the advent of cloud computing and the exponential growth of traffic with it, power consumption has become an increasing concern for data centers. While power-aware algorithms have been investigated quite well in the wireless context, their developments in wire-line networks, especially in traffic-intensive data centers remain to be explored. In this paper, we propose a novel power-aware flow routing algorithm on the premise of performance for fat-tree data center topology and call it GreenFlow. With a maximum link utilization threshold, we route flows as centrally as possible and turn off switches without traffic flowing through them. Moreover, we take load balancing into consideration when rerouting flows on the open devices and their corresponding available paths. Verified by simulation results, our algorithm performs well in typical traffic patterns including uniform traffic, mice-elephant traffic and hotspot traffic. Earning about 20%~58% power reduction when the load is below 0.45, the performance of GreenFlow is still comparable to that of the current best-performing routing algorithms, such as CONGA and LetFlow.
机译:在过去几年中,功耗已成为一个关键问题,特别是互联网,并不可忽略不可忽略。随着云计算的出现和随之交通的指数增长,功耗已成为数据中心的越来越关注。虽然在无线上下文中已经在无线上下文中调查了电源感知算法,但它们在线线路网络的发展仍然仍然探讨。在本文中,我们提出了一种关于脂肪树数据中心拓扑性能的前提下的新颖的动力感知流路由算法,并调用绿色流。通过最大的链路利用率阈值,我们可以尽可能地集中流动,然后关闭交换机,而无需流过它们。此外,当在打开设备上的流程和相应的可用路径上重新路由流程时,我们考虑负载平衡。通过仿真结果验证,我们的算法在典型的交通模式中表现良好,包括统一的流量,小鼠大象流量和热点流量。当负载低于0.45时,赚取约20%〜58%的功率降低,绿色流程的性能仍然与当前最佳的路由算法的性能相当,例如Conga和Letflow。

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