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

1
Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, (16419) 2066, Seobu-Ro, Jangan-Gu, Suwon-Si, Gyeonggi-Do, Korea
2
College of Software, Sungkyunkwan University, (16419) 2066, Seobu-Ro, Jangan-Gu, Suwon-Si, Gyeonggi-Do, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(2), 311; https://doi.org/10.3390/s19020311
Received: 15 December 2018 / Revised: 3 January 2019 / Accepted: 9 January 2019 / Published: 14 January 2019
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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. View Full-Text
Keywords: load balancing; Software Defined Networking; genetic algorithm; Ant Colony Optimization; genetic-Ant Colony Optimization load balancing; Software Defined Networking; genetic algorithm; Ant Colony Optimization; genetic-Ant Colony Optimization
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Xue, H.; Kim, K.T.; Youn, H.Y. Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization. Sensors 2019, 19, 311.

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