Dynamic Load Balancing Improvement in Software-Defined Networks Using Fuzzy Multi-Objective Programming Algorithms
Subject Areas : ICT
Mohammadreza Forghani
1
,
Mohammadreza Soltanaghaei koupaei
2
,
Farsad Zamani Boroujeni
3
1 - Isfahan (Khorasgan) Branch, Islamic Azad University
2 - Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
3 - Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan
Keywords: Software Defined Networking (SDN), Load Balancing, Multi-objective optimization, Fuzzy voting algorithm.,
Abstract :
Software-Defined Networking (SDN) has been recognized as an efficient approach in the field of communication technology, aiming to improve the performance and efficiency of computer networks, thus reducing costs. One of the key challenges in SDN is load balancing among nodes. Solving this challenge leads to improved response time and network performance. Nowadays, various methods have been proposed for load balancing in SDN, but they have not yet reached the ideal state. In this article, a new method is presented to enhance load balancing and reduce response time. This method utilizes multi-objective evolutionary algorithms and fuzzy weighting. In the proposed method, factors such as bandwidth, traffic status, link buffer, and desired router are taken into account, and the best path and router with desired load balancing for information flows are selected with the minimum time. One prominent advantage of this method is the possibility of performing load balancing automatically without the need for human intervention. Experimental results demonstrate that the proposed method shows a significant improvement of approximately 14.8% in response time compared to other methods, while maintaining load balancing in SDNs. By using the proposed method, in addition to improving service quality and user satisfaction, response time will also be enhanced. In summary, the proposed method is introduced as a viable approach in SDNs and exhibits superiority over existing methods.
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