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Fair and Stable Matching Virtual Machine Resource Allocation Method

Liang Dai1, AoSong He1, Guang Sun1,3, Yuxing Pan2,*

1 Hunan University of Finance and Economics, Changsha, 410205, China
2 Changsha University of Science and Technology, Changsha, 410114, China
3 University of Alabama, Tuscaloosa, 35401, USA

* Corresponding Author: Yuxing Pan. Email: email

Intelligent Automation & Soft Computing 2022, 32(3), 1831-1842.


In order to unify the management and scheduling of cloud resources, cloud platforms use virtualization technology to re-integrate multiple computing resources in the cloud and build virtual units on physical machines to achieve dynamic provisioning of resources by configuring virtual units of various sizes. Therefore, how to reasonably determine the mapping relationship between virtual units and physical machines is an important research topic for cloud resource scheduling. In this paper, we propose a fair cloud virtual machine resource allocation method of using the stable matching theory. Our allocation method considers the allocation of resources from both user’s demand and cloud computing resource provider’s request. When multiple users apply for resources, firstly select a user by user priority, and then deal with this user’s task. Because the user priority is dynamic, so as to avoid a user’s long-term share of resources. This strategy makes user task scheduling is relatively fair. On the basis of weighing the fair allocation of user resources, the stable matching between physical machines and virtual machines is achieved. Our simulation experiments especially given that the main focus of the paper is not to develop a very novel algorithm, but to demonstrate our virtual machine resource allocation method, which effectively improves the average utilization rate of computing resources and reduces the operating costs of cloud providers.


Cite This Article

L. Dai, A. He, G. Sun and Y. Pan, "Fair and stable matching virtual machine resource allocation method," Intelligent Automation & Soft Computing, vol. 32, no.3, pp. 1831–1842, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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