• Lalisa Diribsa Raga
• Charanjiv Singh Sarao
• Abhinav Bhandari

Software-defined networking (SDN) transforms network management by separating the control and data planes, allowing for centralized control and dynamic traffic allocation. Real-time load balancing in SDN is computationally intensive, though, and may need to solve NP-hard optimization problems. Round-robin and least connections, common techniques, do not accommodate dynamic traffic, resulting in congestion and inefficiencies. This paper investigates intelligent load-balancing techniques utilizing genetic-ant colony optimization (GA-ACO), deep reinforcement learning (DRL), fuzzy logic, and multi-criteria decision-making (MCDM) for optimized traffic management. Further, evolution in P4 programming and integration of SDN-IoT enhances the flexibility of networks as well as cuts down computational overhead. A comparative evaluation of these approaches reveals that AI-based solutions dramatically surpass static heuristics in terms of decreasing latency, congestion, and misallocation of resources. The research highlights the significance of scalable, real-time, and computation-efficient load-balancing mechanisms for delivering optimal Quality of Service (QoS) and cybersecurity in contemporary SDN networks. Multi-agent AI models and blockchain-based load balancing should be the areas of future research to further increase efficiency and scalability.

Keywords:Genetic-Ant Colony Optimization (GA-ACO), NP-hard optimization, Intelligent Load Balancing, Software-Defined Networking (SDN), Quality of Service (QoS), Blockchain-Based Load Balancing.

"Intelligent Load Balancing Strategies in Software-Defined Networking", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.e463-e474, March-2025, Available :http://www.jetir.org/papers/JETIR2503467.pdf

"Intelligent Load Balancing Strategies in Software-Defined Networking", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppe463-e474, March-2025, Available at : http://www.jetir.org/papers/JETIR2503467.pdf