Adaptive Load Balancing in Cloud Networks Using Large Language Models

Abstract

Adaptive load balancing in cloud networks is crucial for optimizing resource utilization and ensuring system reliability. The integration of large language models (LLMs) into this process offers a novel approach to enhancing load balancing mechanisms. By leveraging the predictive capabilities of LLMs, cloud networks can dynamically adjust their resource allocation based on real-time data and anticipated workloads. This adaptation allows for improved performance and reduced latency, as LLMs can analyze complex patterns and trends in traffic, identify potential bottlenecks, and propose efficient load distribution strategies. Furthermore, LLMs can assist in automating decision-making processes and refining load balancing algorithms, ultimately leading to more resilient and scalable cloud infrastructures. This integration represents a significant advancement in managing cloud network resources effectively, demonstrating the potential of combining artificial intelligence with traditional network management techniques.