负载平衡的联合被动和主动SDN控制器分配

Racha Gouareb, V. Friderikos, H. Aghvami, M. Tatipamula
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引用次数: 4

摘要

在软件定义网络(SDN)中,控制器被认为是与网络整体运行相关的关键网络元素。SDN控制器固有的集中性给网络管理带来了足够的灵活性,但一旦出现拥塞事件或故障,整个系统都会受到影响。在这方面,网络流量的时空变化通过增加控制平面过载时的响应时间来影响网络性能,从而在这方面提出了可靠性和可扩展性问题。在这项工作中,我们的目标是解决控制平面的负载平衡问题。该方法旨在通过为控制器分配开关来平衡多个控制器之间的负载。通过考虑多控制器环境下的被动和主动分配,研究了两种成本。该双目标函数由控制器内负载均衡的代价和流量负载迁移的代价组成。控制器分配问题是一个受计算资源约束的二次规划问题。最后,为了克服由于变量数量增加而导致的维数问题,将最小-最大模型描述为最小化控制器最大负载的混合整数线性规划问题。仿真结果揭示了负载平衡和迁移成本之间的权衡,性能评估显示了与文献中先前提出的算法相比,所提出模型的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint Reactive and Proactive SDN Controller Assignment for Load Balancing
In Software Defined Networks (SDN), the controller is considered as a critical network element with respect to the overall operation of the network. The inherent centralized nature of the SDN controller brings sufficient flexibility to network management, but in the case of congestion episodes or failure, the whole system can be affected. In that respect, the spatiotemporal variation of the network traffic affects the network performance by increasing the response time of the control plane when it is overloaded, raising in that respect the issues of reliability and scalability. In this work, we are aiming to tackle the problem of load balancing in the control plane. The proposed approach aims to balance the load among multiple controllers by assigning switches to controllers. By considering both reactive and proactive assignment in a multi-controller setting, two costs are studied. The bi- objective function is composed of the cost of load balancing within controllers and the cost of traffic load migration. The problem of controller assignment is formulated as a Quadratic Programming, constrained by computing resources. Finally, to overcome the curse of dimensionality due to the increasing number of variables, a min-max model is presented as a mixed-integer linear programming problem minimizing the maximum load of controllers. Simulation results shed light on the trade-off between load balancing and migration cost, and the performance evaluation is demonstrating the efficiency of the proposed model compared to previously proposed algorithms in the literature.
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