Data stable aggregation algorithm based on fuzzy algorithm in cloud computing

Issue title: Information Sciences and Data Transmission of Data

Guest editors: Juan Luis García Guirao

Article type: Research Article

Authors: Wang, Yu^*;

Affiliations: Department of Computer Science, Hanjiang Normal University, Shiyan, China

Correspondence: [*] Corresponding author. Yu Wang, Department of Computer Science, Hanjiang Normal University, 442000, Shiyan, China. Email: [email protected].

Abstract: In order to improve the ability of cloud computing data scheduling, this paper proposes a new method for multi-task, multi-level cloud computing data aggregation based on fuzzy association feature extraction. Heterogeneous directed graph analysis method is used to design cloud computing data. The semantic correlation fusion method is used to implement cloud computing data feature extraction and adaptive scheduling. The fuzzy clustering is used to process the characteristic amount of cloud computing data, and the optimal aggregation of cloud computing data is realized. Simulation results show that the method has a higher recall rate for multi-task and multi-level cloud data aggregation, and the highest recall rate can reach 1, which improves the accuracy of resource aggregation.

Keywords: Cloud computing, fuzzy algorithm, data, stable convergence, clustering

DOI: 10.3233/JIFS-179862

Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 6, pp. 7935-7944, 2020