Controller Location and Load Balancing Integrated Solution

A. Muthanna
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Abstract

The usage of multi-controller SDNs is currently the most efficient approach for constructing the core of communication networks of the fifth and following generations. One of the top priorities in the study of this topic is occupied by the optimisation of the network core construction since it involves relatively high expenses when developing communication networks of the fifth and future generations. Due to the complexity of the problems being tackled, there are currently a number of load balancing algorithms and algorithms for arranging controllers in multicontroller networks that are based on meta-heuristic methods. These algorithms allow for the optimum possible utilisation of controller resources in such networks. However, a comprehensive solution to the load balancing and controller placement issues has yet to be discovered. The answer to such an issue is the focus of this article. The report suggests using network clustering in conjunction with the meta-heuristic chaotic salp swarm technique, which has  shown to be effective in prior research on the challenges of creating multi-controller networks, to accomplish this goal. The salp swarm algorithm in the paper is adjusted to take into account the integral solution to the problem of deploying controllers based on clustering of a multi-controller network and load balancing. By contrasting the simulation results with those from the well-known meta-heuristic particle swarm algorithms optimization and the grey wolf GWO, as well as the previous version of the chaotic salp swarm algorithm CSSA, the effectiveness of the proposed solution was evaluated.
控制器定位和负载均衡集成解决方案
使用多控制器sdn是目前构建第五代及以后通信网络核心的最有效方法。网络核心结构的优化是本课题研究的重点之一,因为在开发第五代和下一代通信网络时,网络核心结构的优化涉及到较高的费用。由于所要解决的问题的复杂性,目前有许多基于元启发式方法的负载平衡算法和多控制器网络中的控制器安排算法。这些算法允许在这种网络中最优地利用控制器资源。然而,一个全面的解决方案的负载平衡和控制器的放置问题尚未被发现。这个问题的答案是本文的重点。该报告建议使用网络聚类与元启发式混沌salp群技术相结合,该技术在先前的研究中证明了创建多控制器网络的挑战是有效的,以实现这一目标。本文对salp群算法进行了调整,以考虑基于多控制器网络聚类和负载均衡的控制器部署问题的整体解决方案。通过与著名的元启发式粒子群算法优化、灰狼GWO算法以及混沌藻群算法CSSA的仿真结果对比,评价了所提方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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