面向云端融合的任务-资源双边匹配决策模型

doi:10.16182/j.issn1004731x.joss.201811036

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (11): 4348-4358.doi: 10.16182/j.issn1004731x.joss.201811036

面向云端融合的任务-资源双边匹配决策模型

程丽军, 王艳

江南大学物联网工程学院,江苏无锡214122

收稿日期:2018-05-18 修回日期:2018-07-02 发布日期:2019-01-04
作者简介:程丽军(1991-),女,安徽合肥,硕士生,研究方向为云智能生产优化调度。
基金资助:
国家自然科学基金(61572238),江苏省杰出青年基金(BK20160001)

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

Cheng Lijun, Wang Yan

College of the Internet of Things, Jiangnan University, Wuxi 214122, China

Received:2018-05-18 Revised:2018-07-02 Published:2019-01-04

摘要/Abstract

摘要： 云任务-资源匹配环节脱节是云端融合过程的突出问题,针对该问题,考虑任务和资源的双边满意度,提出一种基于改进知识迁移极大熵聚类算法(Knowledge transfer maximum entropy clustering algorithm,KT-MECA)的云端融合任务分配模式。该算法改进了历史聚类中心知识和历史隶属度知识的引入方式,提高了聚类性能和稳定性,解决了传统聚类算法不能适用于动态云资源聚类的问题。并考虑双边主体满意度,将该算法的聚类结果应用于云任务-资源的双边匹配决策优化模型中,通过实例证明该方法是可行的。

关键词: 云端融合, 知识迁移, 极大熵聚类, 双边匹配

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.

Key words: cloud fusion, knowledge transfer, maximum entropy clustering, two-sided matching

中图分类号:

TP391.9

程丽军, 王艳. 面向云端融合的任务-资源双边匹配决策模型[J]. 系统仿真学报, 2018, 30(11): 4348-4358.

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.

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面向云端融合的任务-资源双边匹配决策模型

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

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