Combined optimization strategy: CUBW for load balancing in software defined network

Abstract

Software Defined Network (SDN) facilitates a centralized control management of devices in network, which solves many issues in the old network. However, as the modern era generates a vast amount of data, the controller in an SDN could become overloaded. Numerous investigators have offered their opinions on how to address the issue of controller overloading in order to resolve it. Mostly the traditional models consider two or three parameters to evenly distribute the load in SDN, which is not sufficient for precise load balancing strategy. Hence, an effective load balancing model is in need that considers different parameters. Considering this aspect, this paper presents a new load balancing model in SDN is introduced by following three major phases: (a) work load prediction, (b) optimal load balancing, and (c) switch migration. Initially, work load prediction is done via improved Deep Maxout Network. COA and BWO are conceptually combined in the proposed hybrid optimization technique known as Coati Updated Black Widow (CUBW). Then, the optimal load balancing is done via hybrid optimization named Coati Updated Black Widow (CUBW) Optimization Algorithm. The optimal load balancing is done by considering migration time, migration cost, distance and load balancing parameters like server load, response time and turnaround time. Finally, switch migration is carried out by considering the constraints like migration time, migration cost, and distance. The migration time of the proposed method achieves lower value, which is 27.3%, 40.8%, 24.40%, 41.8%, 42.8%, 42.2%, 40.0%, and 41.6% higher than the previous models like BMO, BES, AOA, TDO, CSO, GLSOM, HDD-PLB, BWO and COA respectively. Finally, the performance of proposed work is validated over the conventional methods in terms of different analysis.