Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning

• URL: http://arxiv.org/abs/2403.13619v1
• Date: Wed, 20 Mar 2024 14:13:44 GMT
• Title: Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning
• Authors: Yulu Gong, Jiaxin Huang, Bo Liu, Jingyu Xu, Binbin Wu, Yifan Zhang,
• Abstract summary: The paragraph is grammatically correct and logically coherent. It emphasizes the need for efficient data access and storage, as well as the utilization of cloud computing migration technology to prevent additional time delays.
• Score: 17.423579410846695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The paragraph is grammatically correct and logically coherent. It discusses the importance of mobile terminal cloud computing migration technology in meeting the demands of evolving computer and cloud computing technologies. It emphasizes the need for efficient data access and storage, as well as the utilization of cloud computing migration technology to prevent additional time delays. The paragraph also highlights the contributions of cloud computing migration technology to expanding cloud computing services. Additionally, it acknowledges the role of virtualization as a fundamental capability of cloud computing while emphasizing that cloud computing and virtualization are not inherently interconnected. Finally, it introduces machine learning-based virtual machine migration optimization and dynamic resource allocation as a critical research direction in cloud computing, citing the limitations of static rules or manual settings in traditional cloud computing environments. Overall, the paragraph effectively communicates the importance of machine learning technology in addressing resource allocation and virtual machine migration challenges in cloud computing.

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