Using Heuristics to the Controller Placement Problem in Software-Defined Multihop Wireless Networking

Afsane Zahmatkesh, Chung-Horng Lung
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Abstract

Solving the controller placement problem (CPP) in an SDN architecture with multiple controllers has a significant impact on control overhead in the network, especially in multihop wireless networks (MWNs). The generated control overhead consists of controller-device and inter-controller communications to discover the network topology, exchange configurations, and set up and modify flow tables in the control plane. However, due to the high complexity of the proposed optimization model to the CPP, heuristic algorithms have been reported to find near-optimal solutions faster for large-scale wired networks. In this paper, the objective is to extend those existing heuristic algorithms to solve a proposed optimization model to the CPP in software-defined multihop wireless networking (SDMWN).Our results demonstrate that using ranking degrees assigned to the possible controller placements, including the average distance to other devices as a degree or the connectivity degree of each placement, the extended heuristic algorithms are able to achieve the optimal solution in small-scale networks in terms of the generated control overhead and the number of controllers selected in the network. As a result, using extended heuristic algorithms, the average number of hops among devices and their assigned controllers as well as among controllers will be reduced. Moreover, these algorithms are able tolower the control overhead in large-scale networks and select fewer controllers compared to an extended algorithm that solves the CPP in SDMWN based on a randomly selected controller placement approach.
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软件定义多跳无线网络中控制器布局问题的启发式研究
在具有多个控制器的SDN体系结构中,解决控制器放置问题(CPP)对网络的控制开销有重要影响,特别是在多跳无线网络(MWNs)中。生成的控制开销包括控制器-设备和控制器间的通信,以发现网络拓扑、交换配置、建立和修改控制平面中的流表。然而,由于所提出的优化模型对CPP的高度复杂性,已有报道称启发式算法可以更快地找到大规模有线网络的近最优解。本文的目标是将现有的启发式算法扩展到软件定义多跳无线网络(SDMWN)中的CPP优化模型。我们的研究结果表明,使用分配给可能的控制器放置的排序度,包括到其他设备的平均距离作为一个度或每个放置的连接度,扩展启发式算法能够在小规模网络中根据生成的控制开销和网络中选择的控制器数量实现最优解。因此,使用扩展启发式算法,设备之间及其分配的控制器之间以及控制器之间的平均跳数将减少。此外,与基于随机选择控制器放置方法解决SDMWN中的CPP的扩展算法相比,这些算法能够降低大规模网络中的控制开销,并选择更少的控制器。
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