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Dynamic resource allocation for virtual machine migration optimization using machine learning
- 1 Northern Arizona University
- 2 Trine University
- 3 Zhejiang University
- 4 Computer Information Technology, Independent Researcher
- 5 Computer Network Engineering, Cisco Systems
- 6 Executive Master of Business Administration, Amazon Connect Technology Services (Beijing) Co. Ltd
* Author to whom correspondence should be addressed.
Abstract
This article delves into the importance of applying machine learning and deep reinforcement learning techniques in cloud resource management and virtual machine migration optimization, highlighting the role of these advanced technologies in dealing with the dynamic changes and complexities of cloud computing environments. Through environment modeling, policy learning, and adaptive enhancement, machine learning methods, especially deep reinforcement learning, provide effective solutions for dynamic resource allocation and virtual intelligence migration. These technologies can help cloud service providers improve resource utilization, reduce energy consumption, and improve service reliability and performance. Effective strategies include simplifying state space and action space, reward shaping, model lightweight and acceleration, and accelerating the learning process through transfer learning and meta-learning techniques. With the continuous progress of machine learning and deep reinforcement learning technologies, combined with the rapid development of cloud computing technology, it is expected that the application of these technologies in cloud resource management and virtual machine migration optimization will be more extensive and in-depth. Researchers will continue to explore more efficient algorithms and models to further improve the accuracy and efficiency of decision making. In addition, with the integration of edge computing, Internet of Things and other technologies, cloud computing resource management will face more new challenges and opportunities, and the application scope and depth of machine learning and deep reinforcement learning technology will also expand, opening new possibilities for building a more intelligent, efficient and reliable cloud computing service system.
Keywords
Cloud computing migration technology, Virtualization, Machine learning-based optimization, Dynamic resource allocation
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Cite this article
Gong,Y.;Huang,J.;Liu,B.;Xu,J.;Wu,B.;Zhang,Y. (2024). Dynamic resource allocation for virtual machine migration optimization using machine learning. Applied and Computational Engineering,57,1-8.
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
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