Journal
JOURNAL OF SUPERCOMPUTING
Volume 73, Issue 10, Pages 4535-4559Publisher
SPRINGER
DOI: 10.1007/s11227-017-2032-8
Keywords
Cloud service providers (CSPs); Cloud users (CUs); Trust measure parameters (TMPs); Rough set theory (RST); Hypergraph; Hypergraph-based computational model (HGCM)
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Funding
- Department of Science and Technology, New Delhi, India for INSPIRE Fellowship [DST/INSPIRE Fellowship/2013/963]
- Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions [SR/FST/ETI-349/2013]
- Department of Science and Technology, New Delhi, India-Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions Government of India [SR/FST/MSI-107/2015]
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Selection of trustworthy cloud services has been a major research challenge in cloud computing, due to the proliferation of numerous cloud service providers (CSPs) along every dimension of computing. This scenario makes it hard for the cloud users to identify an appropriate CSP based on their unique quality of service (QoS) requirements. A generic solution to the problem of cloud service selection can be formulated in terms of trust assessment. However, the accuracy of the trust value depends on the optimality of the service-specific trust measure parameters (TMPs) subset. This paper presents TrustCom-a novel trust assessment framework and rough set-based hypergraph technique (RSHT) for the identification of the optimal TMP subset. Experiments using Cloud Armor and synthetic trust feedback datasets show the prominence of RSHT over the existing feature selection techniques. The performance of RSHT was analyzed using Weka tool and hypergraph-based computational model with respect to the reduct size, time complexity and service ranking.
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