Network load balancing optimization algorithm based on machine learning

As the Internet expands and digital transformation continues, the demand for network services and data transmission has skyrocketed. However, traditional routing algorithms are struggling to adapt to the increasing complexity of network structures, leading to congestion and inefficiency. To address this problem, network load balancing methods are essential. This paper proposes a network load balancing optimization algorithm based on machine learning. Using machine learning techniques, the algorithm intelligently distributes network traffic to reduce congestion and improve overall network performance. The algorithm considers factors such as task types and server processing times to achieve optimal load distribution. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving superior performance compared to existing models.