Accurate and efficient resource allocation based on user intent is an important issue in a large-scale, distributed environment, such as cloud computing. Although a large number of cloud resource matching models he been proposed, these models do not consider the interests of user and cloud service provider objectively and fairly. Thus, a novel resource matching strategy that regulates multiattribute matching between cloud resources and tasks is proposed in this study. Tasks and resources are initially clustered on the basis of the attribute characteristics to reduce the scope of resource retrieval. Then, the tasks are matched to the appropriate resources in terms of the strict bilateral matching algorithm to improve the satisfaction of both parties. Finally, a series of experiments are reported to show the effectiveness of the algorithm. Experimental results conclusively demonstrate that our proposed methods can availably decrease the scheduling overhead and improve the overall satisfaction of both parties simultaneously.

基于用户意图的准确，高效的资源分配是大规模分布式环境（例如云​​计算）中的重要问题。尽管他提出了大量的云资源匹配模型，但是这些模型并未客观公正地考虑用户和云服务提供商的利益。因此，本研究提出了一种新的资源匹配策略，该策略可调节云资源与任务之间的多属性匹配。最初，任务和资源是根据属性特征进行聚类的，以减小资源检索的范围。然后，根据严格的双边匹配算法将任务匹配到适当的资源，以提高双方的满意度。最后，报告了一系列实验以证明该算法的有效性。