AI-Driven Optimization of Cloud Networking for Large Language Model Applications

Abstract

The rapid advancements in artificial intelligence (AI) and the development of large language models (LLMs) have necessitated robust and scalable cloud networking solutions. This paper explores the intersection of AI, LLMs, and cloud networking, focusing on AI-driven optimization techniques to enhance the performance, efficiency, and scalability of cloud infrastructures supporting LLM applications. We review the current state-of-the-art methods in AI optimization and cloud networking, highlighting the challenges and opportunities in deploying LLMs on cloud platforms. Key areas of focus include resource allocation, network management, and load balancing, with an emphasis on real-world applications and case studies. By leveraging AI-driven techniques, we propose novel solutions to optimize cloud network performance, reduce latency, and improve the overall efficiency of LLM deployments. This research aims to provide a comprehensive understanding of how AI can revolutionize cloud networking for large-scale AI applications, paving the way for more efficient and effective deployment of LLMs in various industries.