Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 6th International Conference on Computing and Data Science

Series Vol. 64 , 15 May 2024


Open Access | Article

Application of machine learning optimization in cloud computing resource scheduling and management

Yifan Zhang * 1 , Bo Liu 2 , Yulu Gong 3 , Jiaxin Huang 4 , Jingyu Xu 5 , Weixiang Wan 6
1 Executive Master of Business Administration,Amazon Connect Technology Services (Beijing),Xi’an, Shaanxi, China
2 Software Engineering,Zhejiang University
3 Computer & Information Technology,Northern Arizona University
4 Information Studies,Trine University
5 Computer Information Technology,Northern Arizona University
6 Electronics & Communication Engineering,University of Electronic Science and Technology of China

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 64, 17-22
Published 15 May 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Yifan Zhang, Bo Liu, Yulu Gong, Jiaxin Huang, Jingyu Xu, Weixiang Wan. Application of machine learning optimization in cloud computing resource scheduling and management. ACE (2024) Vol. 64: 17-22. DOI: 10.54254/2755-2721/64/20241359.

Abstract

In recent years, cloud computing has been widely used. Cloud computing refers to the centralized computing resources, users through the access to the centralized resources to complete the calculation, the cloud computing center will return the results of the program processing to the user. Cloud computing is not only for individual users, but also for enterprise users. By purchasing a cloud server, users do not have to buy a large number of computers, saving computing costs. According to a report by China Economic News Network, the scale of cloud computing in China has reached 209.1 billion yuan.Rational allocation of resources plays a crucial role in cloud computing. In the resource allocation of cloud computing, the cloud computing center has limited cloud resources, and users arrive in sequence. Each user requests the cloud computing center to use a certain number of cloud resources at a specific time.

Keywords

Cloud computing, Resource scheduling, Machine learning optimization, Artificial intelligence

References

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2. Q. Zeng, Y. Du, K. Huang, and K. K. Leung, “Energy-efficient resource management for federated edge learning with cpu-gpu heterogeneous computing,” IEEE Transactions on Wireless Communications, vol. 20, no. 12, pp. 7947–7962, 2021.

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5. Brogi A, Forti S, Guerrero C, et al. How to Place Your Apps in the Fog: State of the Art and Open Challenges[J]. Software: Practice and Experience (S0167-739X), 2019, 1(1): 1-8.

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9. Xiaoxi Zhang, Jianyu Wang, Li-Feng Lee, Tom Yang, Akansha Kalra,Gauri Joshi, Carlee Joe-Wong, “Machine Learning on Volatile Instances:Convergence, Runtime, and Cost Trade-offs”, IEEE/ACM Transactions on Networking, 30(1):215—228, 2022

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 6th International Conference on Computing and Data Science
ISBN (Print)
978-1-83558-425-5
ISBN (Online)
978-1-83558-426-2
Published Date
15 May 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/64/20241359
Copyright
15 May 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated