A framework of cloud service selection based on trust mechanism

Yang, Yan; Peng, Xinguang; Fu, Donglai

doi:10.1504/ijahuc.2017.083596

Cited by 12 publications

(6 citation statements)

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“…This model is used to assess the competence of cloud services by analysing the history information related to multiple QoS attributes of the SLA contracted by CSC and CSP. In [13], a novel method was presented, which employed a multi-QoS-aware cloud service selection strategy and the analytic hierarchy process (AHP) method to help the CSCs to select the appropriate cloud service.…”

Section: Related Workmentioning

confidence: 99%

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FASTCloud: A framework of assessment and selection for trustworthy cloud service based on QoS

Li¹

2020

Preprint

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By virtue of technology and benefit advantages, cloud computing has increasingly attracted a large number of potential cloud consumers (PCC) plan to migrate traditional business to the cloud service. However, trust has become one of the most challenging issues that prevent the PCC from adopting cloud services, especially in trustworthy cloud service selection. In addition, due to the diversity and dynamic of quality of service (QoS) in cloud environment, the existing trust assessment methods based on the single constant value of QoS attribute and the subjective weight assignment are not good enough to provides an effective solution for PCCs to identify and select a trustworthy cloud service among a wide range of functionally-equivalent cloud service providers (CSP). To address the challenge, a novel assessment and selection framework for trustworthy cloud service, FASTCloud, is proposed in this study. This framework facilitate PCCs to select a trustworthy cloud service based on their actual QoS requirements. In order to accurately and efficiently assess the trust level of cloud services, a QoS-based trust assessment model is proposed. This model represents a trust level assessment method based on the interval multiple attributes with a objective weight assignment method based on the deviation maximization to adaptively determine the trust level of different cloud services provisioned by candidate CSPs. The advantage of proposed trust level assessment method in time complexity is demonstrated by the performance analysis

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Section: Related Workmentioning

confidence: 99%

“…• Step 2: The weight vector ω of QoS attributes can be determined in this step. Firstly, the total deviation D k (ω) of each CSP from other CSPs on each QoS attribute of normalized decision matrix need to be calculated according to equation ( 12) and (13). Secondly, the D k (ω) need to be maximized by equation (14).…”

Section: Time Complexity Of Tammentioning

confidence: 99%

FASTCloud: A framework of assessment and selection for trustworthy cloud service based on QoS

Li¹

2020

Preprint

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“…Shaikh et al [13] identified some of the available checklists to evaluate and select the parameters of the service provider to select the optimal service. Yang et al [14] proposed a QoS-aware cloud service selection strategy, using AHP to help cloud customers choose the appropriate cloud service. Lartigau et al [15] proposed a method based on intuitionistic fuzzy sets, which can perform similarity evaluation.…”

Section: Related Workmentioning

confidence: 99%

A Cloud Service Trust Evaluation Model Based on Combining Weights and Gray Correlation Analysis

Wang

Wen

Wang

et al. 2019

Security and Communication Networks

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Cloud services are cloud computing resources and applications deployed on the Internet or cloud computing platform, and users can access the required cloud services at any time. However, users face the diversity and complexity of quality of service (QoS) when evaluating and selecting cloud services. Therefore, it is important to study and establish an effective and objective trust model to improve user satisfaction and interaction success rate. In this paper, a model based on combining weights and gray correlation analysis is proposed. Firstly, direct trust, recommendation trust, and reputation together form a comprehensive trust, resulting in a more accurate overall trust. Second, rough set theory and analytic hierarchy process (AHP)-based method are used for the direct trust. Meanwhile, the degree of similarity recommendation trust is calculated by a gray relational analysis method. In order to ensure the accuracy of direct trust, this paper proposes a dynamic trust update mechanism. Finally, the simulation experiment is carried out to verify that the cloud services trust evaluation model (CSTEM) is more robust than the other three methods. It protects against malicious entities; at the same time, it can increase user satisfaction and interaction success rate.

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“…A new trust evaluation framework used to indicate optimal trust metrics [6]. Yang et al [7] proposed a QoS-aware cloud service selection strategy, which uses AHP to select cloud services. Choi and Jeong [8] proposed a system that considers the preferences of each service attribute and selects the service by preference.…”

Section: Related Workmentioning

confidence: 99%

“…Denote the direct trust value of the domain recommender B j for the cloud service CS i as DT B j CS i . If the recommender B j has called the cloud service CS i multiple times, the direct trust value of B j is calculated by equation (7). DT A B j indicates the trust value of user A for the recommender B j in the same domain.…”

Section: A: Intra-domain Recommendation Trustmentioning

confidence: 99%

A Cloud Service Selection Method Based on Trust and User Preference Clustering

et al. 2019

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In order to achieve universal and personalized cloud service choices in a social network environment, we propose a cloud service selection method based on trust and user preference clustering. The method performs a comprehensive trust evaluation, which is used to evaluate and select cloud services. Meanwhile, we propose an improved condensed hierarchical clustering method based on user preference similarity to further improve the accuracy of recommendation trust. A cloud model-based approach is used to measure similarities between users, and then a hierarchical clustering method is used to divide users into different domains according to user similarity. The final recommendation trust will be obtained, which includes the intra-domain recommendation trust and the extra-domain recommendation trust. The comprehensive trust of cloud services, which consists of direct trust and recommended trust. Simulation experiments verify the accuracy and superiority of the clustering algorithm. Experimental results show that the cloud service selection method improves the transaction success rate and enables users to select more satisfactory cloud services.

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A framework of cloud service selection based on trust mechanism

Cited by 12 publications

References 0 publications

FASTCloud: A framework of assessment and selection for trustworthy cloud service based on QoS

FASTCloud: A framework of assessment and selection for trustworthy cloud service based on QoS

A Cloud Service Trust Evaluation Model Based on Combining Weights and Gray Correlation Analysis

A Cloud Service Selection Method Based on Trust and User Preference Clustering

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