Phase-ordering correlators are obtained from Schrödinger covariance of four-point response functions, and the autocorrelation exponent is linked to the passage exponent.
Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning
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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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cs.SE 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Toward Automated Hypervisor Scenario Generation Based on VM Workload Profiling for Resource-Constrained Environments
Phase-ordering correlators are obtained from Schrödinger covariance of four-point response functions, and the autocorrelation exponent is linked to the passage exponent.