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Maximizing Network Utilization for SDN Based on Particle Swarm Optimization

机译:基于粒子群优化的SDN实现网络利用率最大化

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Software Defined Networks (SDNs) allow a centralized controller to globally plan packets forwarding according to the operator's objectives. The realization of global objectives requires more local forwarding rules. However, the forwarding tables in TCAM-based SDN switches are limited resources. In this paper, we concentrate on satisfying global network objectives, such as maximum flow, with the limitation of forwarding table size. We formulate the problem as the Bounded Forwarding-Rules Maximum Flow (BFR-MF) problem. And then, we improve the updating of particles in Particle Swarm Optimization (PSO) by merging particles and propose the PSO-based Maximum Flow (PSO-MF) algorithm to maximize the overall feasible traffic. We maintain fairness among flows to guarantee a certain level of Quality-of-Service (QoS). Extensive simulations show that PSO-MF algorithm performs well in network utilization both for backbone and data center networks.
机译:软件定义的网络(SDNS)允许集中控制器根据操作员的目标转发全局计划。实现全球目标需要更多的本地转发规则。但是,基于TCAM的SDN交换机中的转发表是有限的资源。在本文中,我们专注于满足全球网络目标,例如最大流量,具有转发表尺寸的限制。我们将问题标准为有界转发规则最大流量(BFR-MF)问题。然后,我们通过合并粒子来改善粒子群优化(PSO)中的粒子的更新,并提出基于PSO的最大流量(PSO-MF)算法,以最大化整体可行流量。我们在流动之间保持公平,以保证一定程度的服务质量(QoS)。广泛的仿真表明,PSO-MF算法在网络利用方面对骨干和数据中心网络进行了良好。

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