Large Language Models and Reinforcement Learning for Efficient Load Balancing in Dynamic Cloud Environments

Abstract

Balancing the load in a large-scale distributed computing paradigm is challenging for efficient operation and providing clients with more efficient services. In a cloud-distributed environment, clients and organizations face challenges in maintaining the performance of the applications adjacent to the Quality of Service (QoS) and Service Level Agreement (SLA). The clients and the cloud providers grapple to allocate equal workload among the servers. To handle all these challenges, we have designed a cloud computing framework that includes all popular and current load-balancing techniques together to enhance the cloud services, better computing resource usage, and better guide system workloads through distribution. In this work, we accumulated all the requirements and a comfortable load balancer for cloud environments.  Additionally, we consider the explosive growth of IP networks and wireless communications leading to massive data traffic. To manage this and maintain better traffic management, the implementation of artificial technology (AI) is needed. Compared to AWS ELB, Azure Load Balancer, and GCLB default configurations, our method shows significant gains in throughput efficiency, security, and latency management. The proposed artificial intelligence-based Reinforcement Learning (RL) was designed to reallocate and utilize resources to minimize latency and balancing among servers. The proposed work outperforms better compared to traditional load-balancing algorithms with a response time of 200 ms, resource utilization of 85%, and task completion rate of 98% in high workload conditions enhancing the efficiency and scalability of the cloud environment.