Mitigating Cloud Computing Virtualization Performance Problems with an Upgraded Logical Convergence Strategy,ERICDATA高等教育知識庫
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篇名
Mitigating Cloud Computing Virtualization Performance Problems with an Upgraded Logical Convergence Strategy
並列篇名
Mitigating Cloud Computing Virtualization Performance Problems with an Upgraded Logical Convergence Strategy
作者 Ming Zhao、Zhen Wang、Yalong Li、Xiumei Qin
英文摘要

In the domain of cloud computing and network resource virtualization, existing fusion techniques for containers and virtual machines suffer from high energy consumption, inflexible scheduling requirements, and suboptimal resource utilization. This study critically examined the current methods, accounted for the contemporary requirements, and developed a novel strategy aimed at maximizing resource utilization while minimizing energy consumption. Comprehensive experiments illustrate the superiority of our approach over state-of-the-art fusion strategies such as Kubernetes+Kubevirt and OpenStack+Kubernetes, demonstrating significant reductions in energy consumption, improved resource utilization, and enhanced system performance.

 

起訖頁 133-143
關鍵詞 cloud computing、container、logical convergence、deep learning、network resource virtualization、virtual machine
刊名 電腦學刊  
期數 202312 (34:6期)
DOI 10.53106/199115992023123406010   複製DOI
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