Fuzzy trust evaluation based on consistency intensity for cloud servicesby Ying Huo, Yi Zhuang, Siru Ni



A Trust Evaluation Mechanism Based on Certified Trust and Fuzzy-Derived Reputation

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FcVcA: A fuzzy clustering-based vehicular cloud architecture

Hamid Arkian, Reza Atani, Saman Kamali

Trust-Based Adaptation in Complex Service-Oriented Systems

Florian Skopik, Daniel Schall, Schahram Dustdar


Fuzzy trust evaluation based on consistency intensity for cloud services

Ying Huo, Yi Zhuang and Siru Ni

College of Computer Science and Technology,

Nanjing University of Aeronautics and Astronautic, Nanjing, China


Purpose – The purpose of this paper is to define an evaluation model for cloud services to deal with the fuzzy information and propose a novel fuzzy evaluation method based on consistency intensity to analyze the quantitative value from the fuzzy information.

Design/methodology/approach – The cloud service evaluation framework is constructed, and different trusted indicators for the infrastructure services and the application services are designed, respectively. In the novel fuzzy evaluation method, the interval values can be aggregated by the

Dempster-Shafer Theory and be transformed into the certain value by linguistic discount factor. The consistency intensity is proposed to determine the value of the linguistic discount factor, which can reflect the mainstream opinions in the assessment.

Findings – The proposed method can solve the problem on the analysis and synthesis of the fuzzy evaluation information. An instance of trust evaluation of cloud storage service is illustrated to verify that the proposed method can express the opinions of all evaluators more adequately.

Practical implications – A serial of experiments are carried out on NetLogo, and the results show that the proposed method is practical and efficient.

Originality/value – Instead of obtaining only the qualitative results by the multi-attribute decisionmaking method, the fuzzy evaluation method based on consistency intensity can obtain the quantitative results from the fuzzy information according to linguistic discount factor and consistency intensity.

Keywords World wide web, Fuzzy logic, Consistency intensity, Fuzzy evaluation method,

Trustworthy service

Paper type Research paper 1. Introduction

Cloud computing is an emerging service paradigm for sharing computational and storage resources. With the advantages of dynamic deployment and high scalability, it has received extensive attention from the industry and government (Armbrust et al., 2010). Users can easily obtain computational and storage resources they need from the cloud, as long as pay a little rent to the cloud providers. In this pay-as-you-go mode, companies can reduce their IT operation and maintenance cost without purchasing a large number of hardware and software equipment, so that they can focus on their own business. However, the reduction of investment also results in that the quality of cloud services varies greatly. How to evaluate the trust degree of cloud services accurately is an important issue now.


Vol. 44 No. 1, 2015 pp. 7-24 ©Emerald Group Publishing Limited 0368-492X

DOI 10.1108/K-03-2014-0058

The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0368-492X.htm

This work is supported by the National Natural Science Foundation for Youth of China under

Grant No. 61202351, the National Postdoctoral Fund under Grant No. 2011M500124, funding of

Jiangsu Innovation Program for Graduate Education and the Fundamental Research Funds for the Central Universities under Grant No. CXZZ13_0171. 7

Fuzzy trust evaluation

As an X-as-a-Service (XaaS) based computing model, the cloud encapsulates all types of computing-relevant resources as services to be provided for end users, including Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). Virtualization is the mainly technical foundation of cloud computing, which virtualizes the hardware and platform as service components in the cloud service provisioning model (Huang et al., 2013a). It separates the application services from the underlying cloud infrastructures, thus can significantly enhance the flexibility, diversity, and manageability of cloud service so as to improve the cloud performance.

With the increasing popularity of cloud, the issue of trusted cloud has become a paramount concern for most users. The lack of trust between cloud users and providers has hindered the universal acceptance of cloud. Adding trust concept into the decision-making mechanism for users can actuate the providers to prepare the most trustworthy service to satisfy the demand of users in advance. In recent years, scholars have accomplished a few of researches on trusted cloud service model. These models are proposed to perform trust assessment of cloud services from different aspects, such as the reputation of individual service (Wu et al., 2013), the allocation of resources (Kim et al., 2009), or the trust relation between partner services (Huang et al., 2013b). Some other researches focus on the trust elements like security, reliability, availability (Li and

Du, 2013). However, the evaluation and management of cloud services are becoming more and more complex, thus sometimes the users are unable to make an accurate assessment. All of the above studies can only be deployed to deal with the accurate values. The situation has not been considered when the values are fuzzy.

In the field of multi-attribute decision making (MADM), there have been many researches on the description and processing of fuzzy value. The MADM method is a systematic method used to select the optimal alternative from a number of alternatives to some certain attributes. Sometimes in the real-world situation, decision makers should make a decision under fuzzy environment. In this circumstance, the fuzzy multi-attribute decision making (FMADM) method, based on the fuzzy set theory (Zadeh, 1965), was proposed and has been under a rapid development (Chai et al., 2013;

Herva and Roca, 2013).

The FMADM methods differ primarily according to how to model the fuzzy values and how to aggregate evaluations across attributes to arrive at an overall evaluation. In the former aspect, extensions of the fuzzy sets are proposed to describe and character the fuzzy objective world more exquisitely, including the intuitionistic fuzzy set (Pei and Zheng, 2012), the linguistic fuzzy set (Wang et al., 2012), and the intuitionistic linguistic fuzzy set (Liu and Wang, 2014). In the latter aspect,