Energy consumption optimization for software defined networks considering dynamic traffic
Energy consumption optimization for software defined networks considering dynamic traffic
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考虑动态流量的软件定义网络能耗优化
DOI：
10.1109/cloudnet.2014.6968985
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
A. Timm
中科院分区：
文献类型：
--
作者：
Adam Markiewicz;P. N. Tran;A. Timm
今天的网络硬件（如交换机、路由器）通常是全天候运行的，无论流量如何。这是因为在当前的网络中，控制和数据转发功能都嵌入在同一个设备中，并且所有的L2/L3网络协议都被设计成分布式的方式工作。因此，必须始终打开网络设备来处理流量。这导致全球通信网络的能源消耗非常高。软件定义网络是最近引入的一种新的网络范例，其中控制平面与转发平面物理分离，并移动到全局感知的软件控制器上。因此，可以实时监控流量，并根据负载平衡或QoS增强等特定目标非常快速地重新路由流量。因此，它为提高整体网络性能，特别是能源效率开辟了新的机会。本文提出了一种基于当前通信量负载，重新配置网络以降低能耗的方法。我们的主要想法是打开最少数量的必要交换机/路由器和链路来承载流量。我们首先将该问题表述为一个混合整数线性规划（MILP）问题，并进一步提出了一种启发式方法，称为战略贪婪启发式，具有四种不同的策略来解决大型网络的问题。我们对典型的校园网和具有真实交通信息和能源消耗的任意网状网络进行了大量的仿真，以证明所提出的方法具有潜在的节能潜力。结果表明，我们可以节省高达45%的能源消耗在夜间。
Today's networking hardware (e.g. switches, routers) is typically running 24/7, regardless of the traffic volume. This is because in current networks, the controlling and data forwarding functions are embedded in the same devices, and all L2/L3 network protocols are designed to work in a distributed manner. Therefore, network devices must be switched on all the time to handle the traffic. This consequently results in very high global energy consumption of communication networks. Software Defined Networking was recently introduced as a new networking paradigm, in which the control plane is physically separated from the forwarding plane and moved to a globally-aware software controller. As a consequence, traffic can be monitored in real time and rerouted very fast regarding certain objectives such as load balancing or QoS enhancement. Accordingly, it opens new opportunities to improve the overall network performance in general and the energy efficiency in particular. This paper proposes an approach that reconfigures the network in order to reduce the energy consumption, based on the current traffic load. Our main idea is to switch on a minimum amount of necessary switches/routers and links to carry the traffic. We first formulate the problem as a mixed integer linear programming (MILP) problem and further present a heuristic method, so called Strategic Greedy Heuristic, with four different strategies, to solve the problem for large networks. We have carried out extensive simulations for a typical campus network and arbitrary mesh networks with realistic traffic information and energy consumption, to prove the potential energy saving of the proposed approach. The results showed that we can save up to 45% of the energy consumption at nighttime.