Two-sided Matching Decision Model between Task and Resource for Cloud Fusion

Authors

Lijun Cheng, College of the Internet of Things, Jiangnan University, Wuxi 214122, China;
Wang Yan, College of the Internet of Things, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: The disjointing of cloud task-to-resource matching link is a prominent problem in the cloud fusion process. Aiming at this problem, a cloud-based fusion task assignment model optimization method based on the improved knowledge migration maximum entropy clustering algorithm (KT-MECA) is proposed, in which the two-sided satisfaction of tasks and resources is considered. The algorithm improves the introduction of historical clustering center knowledge and membership degree knowledge, improves clustering performance and stability, and solves the problem that traditional clustering algorithms cannot be applied to dynamic cloud resource clustering. Considering the two-side subject satisfaction, the result of KT-MECA is applied to the two-side matching decision optimization model of cloud task and resources. The example proves that this method is feasible.

Recommended Citation

Cheng, Lijun and Yan, Wang (2019) "Two-sided Matching Decision Model between Task and Resource for Cloud Fusion," Journal of System Simulation: Vol. 30: Iss. 11, Article 36.
DOI: 10.16182/j.issn1004731x.joss.201811036
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss11/36

First Page

4348

Revised Date

2018-07-02

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201811036

Last Page

4358

CLC

TP391.9

Recommended Citation

Cheng Lijun, Wang Yan. Two-sided Matching Decision Model between Task and Resource for Cloud Fusion[J]. Journal of System Simulation, 2018, 30(11): 4348-4358.

DOI

10.16182/j.issn1004731x.joss.201811036