软件定义网络(CPCSA)中具有关键交换机感知功能的控制器布局

Nura Muhammed Yusuf, Kamalrulnizam Bin Abu Bakar, Babangida Isyaku, Abdelzahir Abdelmaboud, W. Nagmeldin
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摘要

软件定义网络(SDN)是一种网络架构,通过将网络决策从数据平面转移到控制平面,从而提高了效率。在传统的 SDN 中,通常使用单个控制器。然而,现代网络因其规模大、流量高、服务质量要求各不相同而变得复杂,这给控制器带来了很高的控制信息通信开销。同样,使用多个分布式控制器的解决方案也带来了 "控制器放置问题"(CPP)。现有的 CPP 技术还没有充分考虑到在网络分区期间将交换机角色纳入 CPP 建模以进行控制器放置的问题。本文提出了基于关键开关意识(CPCSA)的网络分区控制器放置算法。CPCSA 可识别软件定义广域网 (SDWAN) 中的关键交换机,然后根据关键性对网络进行分区。随后,为每个分区分配一个控制器,以改善控制信息的通信开销、损耗、吞吐量和流量设置延迟。CPSCSA 利用从互联网拓扑动物园(Internet Topology Zoo)获得的真实网络拓扑进行了实验。结果表明,与基准算法相比,CPCSA 的控制器开销总体减少了 73%,损耗减少了 51%,延迟减少了 16%,同时吞吐量提高了 16%。
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Controller placement with critical switch aware in software-defined network (CPCSA)
Software-defined networking (SDN) is a networking architecture with improved efficiency achieved by moving networking decisions from the data plane to provide them critically at the control plane. In a traditional SDN, typically, a single controller is used. However, the complexity of modern networks due to their size and high traffic volume with varied quality of service requirements have introduced high control message communications overhead on the controller. Similarly, the solution found using multiple distributed controllers brings forth the ‘controller placement problem’ (CPP). Incorporating switch roles in the CPP modelling during network partitioning for controller placement has not been adequately considered by any existing CPP techniques. This article proposes the controller placement algorithm with network partition based on critical switch awareness (CPCSA). CPCSA identifies critical switch in the software defined wide area network (SDWAN) and then partition the network based on the criticality. Subsequently, a controller is assigned to each partition to improve control messages communication overhead, loss, throughput, and flow setup delay. The CPSCSA experimented with real network topologies obtained from the Internet Topology Zoo. Results show that CPCSA has achieved an aggregate reduction in the controller’s overhead by 73%, loss by 51%, and latency by 16% while improving throughput by 16% compared to the benchmark algorithms.
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