Willow: Saving Data Center Network Energy for Network-Limited Flows
Willow: Saving Data Center Network Energy for Network-Limited Flows
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DOI：
10.1109/tpds.2014.2350990
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发表时间：
2015-09
影响因子：
5.3
通讯作者：
Dan Li;Yirong Yu;Wu He;K. Zheng;Bingsheng He
中科院分区：
文献类型：
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作者：
Dan Li;Yirong Yu;Wu He;K. Zheng;Bingsheng He
如今的大型数据中心非常耗电。数据中心节能不仅有助于控制运营成本，还有利于云服务的可持续增长。由于现代数据中心采用了更多的交换机以及成熟的服务器端电源管理技术，数据中心网络的节能变得越来越重要。以前大多数关于节省数据中心网络能源的工作都集中在将流量聚合到尽可能少的交换机上。然而，在本文中，我们认为，这种方法可能不适用于网络有限的流量，其中的吞吐量是弹性的竞争流的基础上。为了节省这种弹性流所消耗的网络能量，我们提出了一种称为杨柳的流调度方法，该方法同时考虑了所涉及的交换机的数量和它们的活跃工作时间。我们制定了这个问题的编程和设计一个贪婪的近似算法调度流在网上的方式。基于MapReduce轨迹的仿真结果表明，在典型环境下，与ECMP调度相比，杨柳算法可以节省高达60%的网络能量，并且性能优于模拟退火和粒子群优化等经典启发式算法.实验表明，这种动态的能量有效的流调度引起上层应用程序的影响可以忽略不计。
Today's giant data centers are power hungry. Data center energy saving not only helps control the operational cost, but also benefits the sustainable growth of cloud services. Due to the adoption of much more switches in modern data centers as well as the mature server-side power management techniques, energy saving for the data center network is becoming increasingly important. Most previous works on saving data center network energy focus on aggregating flows to as few switches as possible. However, in this paper we argue that this method may not work for network-limited flows, the throughputs of which are elastic based on the competing flows. To save the network energy consumed by this kind of elastic flows, we propose a flow scheduling approach called Willow, which takes both the number of switches involved and their active working durations into consideration. We formulate this problem by programming and design a greedy approximate algorithm to schedule flows in an online manner. Simulations based on MapReduce traces show that Willow can save up to 60 percent network energy compared with ECMP scheduling in typical settings, and outperforms other classical heuristic algorithms such as simulated annealing and particle swarm optimization. Testbed Experiments demonstrate that this kind of dynamic energy-efficient flow scheduling causes negligible impact on upper-layer applications.