Adaptive Load Balancing in Computer Networks Using AI
TL;DR
This work proposes an Adaptive Load Balancing approach using Artificial Intelligence (AI) to dynamically distribute network traffic across multiple servers or network nodes by continuously learning from network conditions such as traffic load, response time, and bandwidth usage.
Abstract
With the rapid growth of cloud computing, data centers, and high-speed networks, efficient load balancing has become a critical requirement for ensuring optimal network performance and quality of service. Traditional load balancing techniques such as round-robin and least-connection methods rely on static rules and fail to adapt to dynamic network conditions like fluctuating traffic, node failures, and varying resource availability. This often leads to congestion, increased latency, and underutilization of network resources. This work proposes an Adaptive Load Balancing approach using Artificial Intelligence (AI) to dynamically distribute network traffic across multiple servers or network nodes. By continuously learning from network conditions such as traffic load, response time, and bandwidth usage, the AI-based system makes intelligent routing decisions in real time. The proposed system is implemented using Python, simulating network environments and applying machine learning algorithms to achieve efficient, scalable, and self-adaptive load balancing in computer networks. Keywords— Adaptive Load Balancing, Artificial Intelligence in Networking, AI-Based Load Balancing,Computer Network Optimization