Intelligent Network Optimization in Cloud Environments with Generative AI and LLMs

Authors

  • Kapil Patil Oracle, Seattle, Washington, USA
  • Bhavin Desai Google, Sunnyvale, California USA

Keywords:

Artificial intelligence, ; Neural networks, Machine learning LLM, Cloud computing, Cloud Infrastructure, Next generation networking, Network architecture, Deep Learning

Abstract

This paper represents a groundbreaking paradigm shift in network optimization. Departing from traditional static methodologies, this innovative approach harnesses the power of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) to optimize cloud networks dynamically. By integrating advanced AI algorithms, this framework continuously adapts and evolves, ensuring optimal real-time performance. This dynamic optimization enhances efficiency and resilience, allowing cloud networks to adjust seamlessly to changing demands and conditions. Through the fusion of cutting-edge technology and adaptive intelligence, this approach heralds a new era in network optimization, empowering organizations to achieve unprecedented levels of agility and scalability in their cloud infrastructures.             

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Published

2024-06-17

How to Cite

Patil, K., & Desai, B. (2024). Intelligent Network Optimization in Cloud Environments with Generative AI and LLMs. Integrated Journal of Science and Technology, 1(2). Retrieved from https://ijstpublication.com/index.php/ijst/article/view/8