Energy‐aware dynamic‐link load balancing method for a software‐defined network using a multi‐objective artificial bee colony algorithm and genetic operators. Issue 18 (13th October 2020)

• Record Type: Journal Article
• Authors: Neghabi, Ali Akbar; Navimipour, Nima Jafari; Hosseinzadeh, Mehdi; Rezaee, Ali
• Is Part Of: IET communications. Volume 14:Issue 18(2020)
• Page Start: 3284
• Page End: 3293
• Publication Date: 2020-10-13
• Subjects: resource allocation -- power consumption -- ant colony optimisation -- telecommunication network routing -- genetic algorithms -- software defined networking -- power aware computing -- telecommunication congestion control; link congestion -- link load balancing strategy -- genetic‐ant colony optimisation -- energy‐aware dynamic‐link load balancing method -- software‐defined network -- ICT -- SDN controller -- energy‐aware dynamic routing method -- multiobjective multiobjective artificial bee colony algorithm -- information and communication technology -- maximum scalability -- queueing time -- NP‐complete problem -- metaheuristic approach -- power consumption; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2019.1300
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : Information and communication technology (ICT) is one of the sectors that have the highest energy consumption worldwide. It implies that the use of energy in the ICT must be controlled. A software‐defined network (SDN) is a new technology in computer networking. It separates the control and data planes to make networks more programmable and flexible. To obtain maximum scalability and robustness, load balancing is essential. The SDN controller has full knowledge of the network. It can perform load balancing efficiently. Link congestion causes some problems such as long transmission delay and increased queueing time. To overcome this obstacle, the link load balancing strategy is useful. The link load‐balancing problem has the nature of NP‐complete; therefore, it can be solved using a meta‐heuristic approach. In this study, a novel energy‐aware dynamic routing method is proposed to solve the link load‐balancing problem while reducing power consumption using the multi‐objective artificial bee colony algorithm and genetic operators. The simulation results have shown that the proposed scheme has improved packet loss rate, round trip time and jitter metrics compared with the basic ant colony, genetic‐ant colony optimisation, and round‐robin methods. Moreover, it has reduced energy consumption.