Qi Yue^1,2,*, Zhibin Deng²

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1603-1623, 2023, DOI:10.32604/cmes.2022.022956

Abstract In this paper, a stable two-sided matching (TSM) method considering the matching intention of agents under a hesitant fuzzy environment is proposed. The method uses a hesitant fuzzy element (HFE) as its basis. First, the HFE preference matrix is transformed into the normalized HFE preference matrix. On this basis, the distance and the projection of the normalized HFEs on positive and negative ideal solutions are calculated. Then, the normalized HFEs are transformed into agent satisfactions. Considering the stable matching constraints, a multiobjective programming model with the objective of maximizing the satisfactions of two-sided agents is constructed. Based on the agent… More >

Liang Dai¹, AoSong He¹, Guang Sun^1,3, Yuxing Pan^2,*

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1831-1842, 2022, DOI:10.32604/iasc.2022.022438

Abstract 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… More >