Title: A dynamic cloud service selection model based on trust and SLA in cloud computing

Authors: Yubiao Wang; Junhao Wen; Quanwang Wu; Lei Guo; Bamei Tao

Addresses: School of Computer Science, Chongqing University, Chongqing 400030, China ' School of Computer Science, Chongqing University, Chongqing 400030, China ' School of Computer Science, Chongqing University, Chongqing 400030, China ' School of Construction Management and Real Estate, Chongqing University, Chongqing 400030, China ' School of Construction Management and Real Estate, Chongqing University, Chongqing 400030, China

Abstract: For high quality and trusted service selection problem, we propose a Dynamic Cloud Services Selection Model (DCSSM). Cloud service resources are divided into different service levels by Service-Level Agreement Management (SLAM). Each SLAM manages some cloud service registration information. In order to make the final trust evaluation values more practical, the model performs a comprehensive trust, which consists of direct trust and indirect trust. Firstly, combining weights consist of subjective weight and objective weight. Using rough sets to calculate objective weight, an Analytic Hierarchy Process (AHP) method is used to calculate subjective weight. The direct trust also considers transaction time and transaction amount, and then gets an accurate direct trust. Secondly, indirect trust considers user trust evaluation similarity, and it contains indirect trust of friends and indirect trust of strangers. Finally, when the transaction was completed, a direct trust dynamic update was proposed. The model is simulated using CloudSim. It was compared with three other methods, and the experimental results showed that the DCSSM performs better than the other three methods.

Keywords: dynamic cloud service; trust; SLA; service-level agreement; selection model; combining weights.

DOI: 10.1504/IJGUC.2019.100874

International Journal of Grid and Utility Computing, 2019 Vol.10 No.4, pp.334 - 343

Received: 10 May 2017
Accepted: 02 Dec 2017
Published online: 19 Jul 2019 *

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