A Trust Evaluation Mechanism for Collaboration of Data-Intensive Services in Cloud

Huang, Longtao; Deng, Shuiguang; Li, Ying; Wu, Jian; Yin, Jianwei; Li, Gexin

doi:10.12785/amis/071l18

Cited by 16 publications

(9 citation statements)

References 14 publications

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“…The fifth parameter configuration is c1 = 1 -(t/T) 3 , c2 = (t/T) 3 , v = [Tt)/T] 2 . These configurations vary the value of parameter n to yield different results when using the decreasing function and the increasing function discussed in the assumptions above.…”

Section: Experiments Evaluationmentioning

confidence: 99%

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Applying improved particle swarm optimization for dynamic service composition focusing on quality of service evaluations under hybrid networks

Gao

Zhang

Yang

et al. 2018

International Journal of Distributed Sensor Networks

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Hybrid services use different protocols on various networks, such as WIFI networks, Bluetooth networks, 5G communications systems, and wireless sensor networks. Hybrid service compositions can be varied, representing an effective method of integrating into wireless scenarios context-aware applications that can sense mobility via changes in user location and combining services to support target functions. In this article, improved particle swarm optimization is introduced into the quality service evaluation of dynamic service composition to meet the mobility requirements of hybrid networks. First, this work classifies hybrid services into different task groups to generate candidate sets and then interface matching is used to compare the operations of candidate services with user requirements to select the appropriate services. Second, the service composition is determined by the particle swarm optimization simulation process, which aims to identify an optimal plan based on the calculated value from quality of service. Third, considering a change of service repository, when the quality of a composite service is lower than a predefined threshold, the local greedy algorithm and global reconfiguration method are adopted to dynamically restructure composite services. Finally, a set of experiments is conducted to demonstrate the effectiveness of the proposed method for determining the dynamic service composition, particularly when the scale of hybrid services is large. The method provides a technical reference for engineering practice that will fulfill mobile computing needs.

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Section: Experiments Evaluationmentioning

confidence: 99%

“…1 -(t/T) 3 (t/T) 3 [(T -t)/T] 2 IPSO: improved particle swarm optimization. (Negative Selection Algorithm 42 ); this experiment seeks to analyze which method is the most suitable for hybrid networks.…”

Section: Experiments Evaluationmentioning

confidence: 99%

Applying improved particle swarm optimization for dynamic service composition focusing on quality of service evaluations under hybrid networks

Gao

Zhang

Yang

et al. 2018

International Journal of Distributed Sensor Networks

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“…Reference [18] proposes a trustworthy selection framework for cloud service selection named TRUSS, which contains an integrated trust evaluation method via combining objective trust assessment and subjective trust assessment. Paper [41] proposes a novel trust evaluation method named UsageQoS for accurately measuring quality of cloud services via leveraging service QoS parameters and user ratings. Although objective assessment methods can yield more accurate results and are easier to implement through existing technical means, they ignore the fact that security is one of the major barriers for adoption of cloud computing and the paramount consideration for CSCs.…”

Section: Related Workmentioning

confidence: 99%

SCCAF: A Secure and Compliant Continuous Assessment Framework in Cloud‐Based IoT Context

Jin

Wang

et al. 2018

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) offers a wide variety of benefits to our daily lives in many ways, ranging from smart wearable devices to industrial systems. However, it also brings well-known security and compliance concerns, especially in the physical layer. In addition, due to numerous IoT architectures which have been developed and deployed based on the cloud, the security and compliance of IoT depend on the cloud thoroughly. In this paper, a secure and compliant continuous assessment framework (SCCAF) is proposed to evaluate the security and compliance levels of cloud services in life-cycle. The SCCAF facilitates cloud service to customers to select an optimal cloud service provider (CSP) which satisfies their desired security requirements. Moreover, it also enables cloud service customers to evaluate the compliance of the selected CSP in the process of using cloud services. To evaluate the performance and availability of SCCAF, we carry out a series of experiments with case study and real-world scenario datasets. Experimental results show that SCCAF can assess the security and compliance of CSPs efficiently and effectively.

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“…People realized the importance of trusted service model, there have been a few of researches on the trust evaluation of cloud service. The criteria are reputation (Wu et al, 2013), trust relation (Huang et al, 2013b), and security, reliability, availability (Li and Du, 2013). However, these researches are evaluating the service from one or several aspects, without conducting a comprehensive assessment of the trust of services.…”

Section: Trusted Indicators Of Servicementioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy trust evaluation based on consistency intensity for cloud services

Zhuang

2015

Kybernetes

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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.

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A Trust Evaluation Mechanism for Collaboration of Data-Intensive Services in Cloud

Cited by 16 publications

References 14 publications

Applying improved particle swarm optimization for dynamic service composition focusing on quality of service evaluations under hybrid networks

Applying improved particle swarm optimization for dynamic service composition focusing on quality of service evaluations under hybrid networks

SCCAF: A Secure and Compliant Continuous Assessment Framework in Cloud‐Based IoT Context

Fuzzy trust evaluation based on consistency intensity for cloud services

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