Reinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments

TL;DR

This paper introduces a reinforcement learning framework for adaptive load balancing in cloud environments, improving response times and resource utilization by dynamically adjusting to workload fluctuations.

Contribution

It presents a novel RL-based load balancing method that learns and adapts in real-time, outperforming traditional static algorithms in cloud resource management.

Findings

Outperforms traditional algorithms in response time

Enhances resource utilization efficiency

Demonstrates adaptability to workload changes

Abstract

Efficient load balancing is crucial in cloud computing environments to ensure optimal resource utilization, minimize response times, and prevent server overload. Traditional load balancing algorithms, such as round-robin or least connections, are often static and unable to adapt to the dynamic and fluctuating nature of cloud workloads. In this paper, we propose a novel adaptive load balancing framework using Reinforcement Learning (RL) to address these challenges. The RL-based approach continuously learns and improves the distribution of tasks by observing real-time system performance and making decisions based on traffic patterns and resource availability. Our framework is designed to dynamically reallocate tasks to minimize latency and ensure balanced resource usage across servers. Experimental results show that the proposed RL-based load balancer outperforms traditional algorithms in terms of response time, resource utilization, and adaptability to changing workloads. These findings highlight the potential of AI-driven solutions for enhancing the efficiency and scalability of cloud infrastructures.