Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization

Abstract

Load Balancing (LB) is one of the most important tasks required to maximize network performance, scalability and robustness. Nowadays, with the emergence of Software-Defined Networking (SDN), LB for SDN has become a very important issue. SDN decouples the control plane from the data forwarding plane to implement centralized control of the whole network. LB assigns the network traffic to the resources in such a way that no one resource is overloaded and therefore the overall performance is maximized. The Ant Colony Optimization (ACO) algorithm has been recognized to be effective for LB of SDN among several existing optimization algorithms. The convergence latency and searching optimal solution are the key criteria of ACO. In this paper, a novel dynamic LB scheme that integrates genetic algorithm (GA) with ACO for further enhancing the performance of SDN is proposed. It capitalizes the merit of fast global search of GA and efficient search of an optimal solution of ACO. Computer simulation results show that the proposed scheme substantially improves the Round Robin and ACO algorithm in terms of the rate of searching optimal path, round trip time, and packet loss rate.

Energy‐aware dynamic‐link load balancing method for a software‐defined network using a multi‐objective artificial bee colony algorithm and genetic operators

“…In this section, a series of simulations have been done to evaluate the performance of MOABC‐GAO. The MOABC‐GAO has been compared with the G‐ACO [13], basic ACO [13], SDNRouting [10], and RR to confirm the efficacy of our proposed design. Section 6.1 describes the simulation setting.…”

“…Finally, Xue et al [13] have proposed a genetic‐ACO (G‐ACO) scheme for load‐balancing in the SDN. The ACO algorithm with the crossover, selection, and mutation operation of GA has been combined.…”

A hybrid Meta-heuristic solution for Energy-Efficient routing in Software-Defined Wireless Sensor Network 

“…Therefore, research is continued to obtain a better solution to meet certain requirements as stated in section 1. Several hybrid solutions e.g., [11,[34][35][36][37], integrated G-ACO [38] have therefore been proposed to solve a complex problem in the various fields in order to achieve improved system performance.…”

AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in Distributed Fragmentation Optimized SDN-IoT

“…EASM is not suitable for switch migration in a large scale network environment. Authors in [33] have focused on dynamic load balancing with hybrid genetic and ant colony optimization algorithms. In GA, the fitness value is computed by path length, energy consumption rate for packets transmission and reception, and energy status of all switches.…”