Vol. 4 No. 1 (2024): January−June 2024
Articles

Applications of Large Language Models in Cloud Computing: An Empirical Study Using Real-world Data

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  • Hanzhe Li,
  • Sherry X Wang,
  • Fu Shang,
  • Kaiyi Niu,
  • Runze Song
Hanzhe Li
Computer Engineering, New York University, New York, USA
Sherry X Wang
Data Processing and Analysis Techniques, University of Hawaii at Manoa, Austin, Texas
Fu Shang
Data Science, New York University, NY, USA
Kaiyi Niu
Artificial intelligence, Royal Holloway University of London, Egham, UK
Runze Song
Information System & Technology Data Analytics, California State University, CA, USA

Published 2024-05-29

Keywords

  • Large Language Models,
  • Cloud Computing,
  • Bayesian Inference,
  • Markov Decision Processes

How to Cite

Li, H., Wang, S. X., Shang, F., Niu, K., & Song, R. (2024). Applications of Large Language Models in Cloud Computing: An Empirical Study Using Real-world Data. Spectrum of Research, 4(1). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/8

Copyright (c) 2024 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This study investigates the integration of Large Language Models (LLMs) in cloud computing, focusing on their impact on resource allocation and management. The research employs Bayesian inference and Markov Decision Processes (MDPs) to enhance predictive accuracy and decision-making efficiency. Over a month, data collected from AWS, GCP, Azure, IBM, and Oracle reveals significant improvements in CPU utilization, memory usage, network latency, and storage performance. LLMs demonstrated superior performance compared to traditional models, optimizing task scheduling and reducing idle times. Bayesian inference refined resource predictions, while MDPs provided a structured approach to dynamic optimization, resulting in lower latency and better system efficiency. The findings suggest that integrating LLMs can transform cloud service management, offering enhanced performance, reliability, and cost savings. Future research should explore long-term trends, security implications, and the ethical aspects of AI deployment in cloud environments.

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