In this research paper, we delve into the innovative realm of optimizing load balancing in Data Center Networks (DCNs) by leveraging the capabilities of Software-Defined Networking (SDN) and machine learning algorithms.

Traditional DCN architectures face significant challenges in handling unpredictable traffic patterns, leading to bottlenecks, network congestion, and suboptimal utilization of resources. Our study proposes a novel framework that integrates the flexibility and programmability of SDN with the predictive and analytical prowess of machine learning.