Multifaceted trust management framework based on a trust level agreement in a collaborative cloud. (April 2017)

• Record Type: Journal Article
• Authors: P, Varalakshmi; T, Judgi
• Is Part Of: Computers & electrical engineering. Volume 59(2017)
• Page Start: 110
• Page End: 125
• Publication Date: 2017-04
• Subjects: Cloud computing -- Collaborative cloud -- Trustworthy service provider -- Service Level Agreement (SLA) -- Trust Level Agreement (TLA) -- Trust estimation; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.10.002
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: The collected in-context feedback is used to improve the consistency and accuracy of the trust computation and selection of cloud service provider. The model selects a trustworthy provider after filtering malicious feedback. The proposed weight calculation method is most suitable for a dynamic environment. The proposed model also facilitates mutual sharing of revenue among CSPs. The proposed research model significantly increases user satisfaction and job-success rate. Abstract: Cloud computing is an emerging field aimed at providing any type of service on demand -anywhere, and anytime. Advances in this field have facilitated a futuristic collaborative cloud in which many inter-connected clouds provide scalable services. A request processed by an untrustworthy provider can endanger quality of service, prompting the dissatisfaction of the customer. This paper aims to select the Trustworthy Service Provider (TSP) by evaluating trust based on in-context feedback from different sources; these sources include customer feedback, global advisory feedback and third-party feedback. Furthermore, unfair feedback is filtered to improve accuracy. Consequently, the job-success rate increases, and customers are able to obtain value-for-money for the sum paid toward selection of a provider. The present experimental work proves that the proposed model is effective and efficient in selecting a trustworthy provider in a collaborative environment. Graphical abstract: