Gongming Zhao, Hongli Xu, Shigang Chen, Liusheng Huang, Pengzhan Wang
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引用次数: 9
摘要
SDN (Software Defined Networking)是一种将控制平面与数据平面分离的网络技术,可以简化网络管理并提供更灵活的报文路由。控制平面通过每个交换机上的转发表(通常包括流表和组表)与数据平面进行交互。由于三元内容可寻址存储器(TCAM)的高成本和高功耗,商品交换机只能支持有限大小的流/组表,这对SDN扩展到大型网络提出了严峻的挑战。解决可伸缩性问题的一种有希望的方法是部署由通配符转发规则指定的聚合默认路径。然而,在众多系统参数和性能/可伸缩性考虑因素之间的多维交互使得在所有交换机上设置流/组表以实现默认路径的最佳总体布局的问题非常具有挑战性。本文研究了大型sdn复杂设置下的流/群表联合优化问题。我们将这个问题化为一个整数线性规划,并证明了它的np -硬度。提出了一种具有有界近似因子的有效算法来解决这一问题。对算法的性质进行了形式化分析。我们在SDN测试平台上实现了所提出的算法进行实验研究,并使用模拟进行大规模调查。实验结果和仿真结果表明了该算法的有效性。
Deploying default paths by joint optimization of flow table and group table in SDNs
Software Defined Networking (SDN) separates the control plane from the data plane to ease network management and provide flexibility in packet routing. The control plane interacts with the data plane through the forwarding tables, usually including a flow table and a group table, at each switch. Due to high cost and power consumption of Ternary Content Addressable Memory (TCAM), commodity switches can only support flow/group tables of limited size, which presents serious challenge for SDN to scale to large networks. One promising approach to address the scalability problem is to deploy aggregate default paths specified by wildcard forwarding rules. However, the multi-dimensional interaction among numerous system parameters and performance/scalability considerations makes the problem of setting up the flow/group tables at all switches for optimal overall layout of default paths very challenging. This paper studies the joint optimization of flow/group tables in the complex setting of large-scale SDNs. We formulate this problem as an integer linear program, and prove its NP-Hardness. An efficient algorithm with bounded approximation factors is proposed to solve the problem. The properties of our algorithm are formally analyzed. We implement the proposed algorithm on an SDN testbed for experimental studies and use simulations for large-scale investigation. The experimental results and simulation results demonstrate high efficiency of our proposed algorithm.