优化软件定义网络中能量感知路由的规则放置

F. Giroire, J. Moulierac, T. K. Phan
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引用次数: 144

摘要

软件定义网络(SDN),特别是OpenFlow,是一种新的网络范例,通过网络可编程性实现创新。在过去的几年中,许多应用程序都是使用SDN构建的,例如服务器负载平衡、虚拟机迁移、流量工程和访问控制。在本文中,我们重点研究了将SDN用于能量感知路由(EAR)。由于流量负载对路由器功耗的影响很小,EAR允许将未使用的链路置于休眠模式以节省能源。SDN可以采集流量矩阵,然后以最小的能耗计算出满足QoS的路由解决方案。然而,先前关于EAR的工作已经假设OpenFlow交换机的表可以容纳无限数量的规则。在实践中,这个假设并不成立,因为流表是用三元内容可寻址内存(TCAM)实现的,这是昂贵和耗电的。在本文中,我们提出了一种优化方法,以最小化骨干网的能量消耗,同时尊重链路的容量约束和路由器的规则空间约束。详细地,我们用整数线性规划(ILP)给出了一个精确的公式,并引入了高效的贪婪启发式算法。基于仿真,我们证明了使用这种智能规则空间分配,可以节省与经典EAR方法几乎相同的功耗。
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Optimizing rule placement in software-defined networks for energy-aware routing
Software-defined Networks (SDN), in particular OpenFlow, is a new networking paradigm enabling innovation through network programmability. Over past few years, many applications have been built using SDN such as server load balancing, virtual-machine migration, traffic engineering and access control. In this paper, we focus on using SDN for energy-aware routing (EAR). Since traffic load has a small influence on power consumption of routers, EAR allows to put unused links into sleep mode to save energy. SDN can collect traffic matrix and then computes routing solutions satisfying QoS while being minimal in energy consumption. However, prior works on EAR have assumed that the table of OpenFlow switch can hold an infinite number of rules. In practice, this assumption does not hold since the flow table is implemented with Ternary Content Addressable Memory (TCAM) which is expensive and power-hungry. In this paper, we propose an optimization method to minimize energy consumption for a backbone network while respecting capacity constraints on links and rule space constraints on routers. In details, we present an exact formulation using Integer Linear Program (ILP) and introduce efficient greedy heuristic algorithm. Based on simulations, we show that using this smart rule space allocation, it is possible to save almost as much power consumption as the classical EAR approach.
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