Toward trustworthy cloud service selection: A time-aware approach using interval neutrosophic set

Ma, Hua; Li, Keqin; Zhang, Hongyu

doi:10.1016/j.jpdc.2016.05.008

Cited by 69 publications

(29 citation statements)

References 53 publications

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“…Existing approaches such as content‐based, memory‐based, and trust‐based are generally continuous. Shifting of service bias and shifting of user preferences are also the most used time effects in categorical TARS.…”

Section: Time Information In Service Recommendationmentioning

confidence: 99%

“…Over the last decade, the number of available Web services has become extremely huge. To provide consumers with high‐quality services that meet their functional and non‐functional requirements, service selection was known, in the literature, as a natural filtering solution that takes into consideration users' preferences and environmental/contextual constraints (eg, QoS levels, time, location, connectivity, etc) . However, after this filtering step, a large number of high‐quality services persist.…”

Section: Introductionmentioning

confidence: 99%

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Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Fayala¹,

Mezni

2019

Concurrency and Computation

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Summary With the growing number of functionally similar services over the Internet, recommendation techniques become a natural choice to cope with the challenging task of optimal service selection, and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation are static, while in the real world, the perception and popularity of Web services may continually change. Time is becoming an increasingly important factor in recommender systems since time effects influence users' preferences to a large extent. In order to help users with this problem, we propose a time‐aware Web service recommendation system. First, we use K‐means clustering method in order to exclude the less similar users, which share few common Web services with the active user at different times. Slope One algorithm is also adopted in order to deal with data sparsity problem by predicting the missing ratings over time. Then, a recommendation algorithm is presented in order to recommend the top‐rated Web services. Experiments proved the accuracy of our approach compared to five existing solutions.

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Section: Time Information In Service Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Fayala¹,

Mezni

2019

Concurrency and Computation

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“…The most employed approaches include multicriteria decision analysis-based service selection [7,[12][13][14], reputation-aware service selection [15], adaptive learning mechanism-based service selection [8,16], economic theoretical model-based service selection [17,18], service level agreement-based service ranking [6], visualization framework for service selection [19], and trust evaluation middleware for cloud service selection [20]. Though these approaches can efficiently measure service quality, the implementation of some approaches is time-consuming and costly.…”

Section: Related Workmentioning

confidence: 99%

“…Ma et al [7] propose a time-aware service selection approach by using interval neutrosophic set. In the paper, the strategy for selecting trustworthy services from an abundant field of candidates involves formulating the problem of timeaware service selection with tradeoffs between performancecosts and potential risk as a multicriterion decision-making (MCDM) problem that creates a ranked services list using interval neutrosophic set (INS) theory.…”

Section: Related Workmentioning

confidence: 99%

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Context‐Aware Cloud Service Selection Model for Mobile Cloud Computing Environments

2018

Wireless Communications and Mobile Computing

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Mobile cloud computing (MCC) has attracted extensive attention in recent years. With the prevalence of MCC, how to select trustworthy and high quality mobile cloud services becomes one of the most urgent problems. Therefore, this paper focuses on the trustworthy service selection and recommendation in mobile cloud computing environments. We propose a novel service selection and recommendation model (SSRM), where user similarity is calculated based on user context information and interest. In addition, the relational degree among services is calculated based on PropFlow algorithm and we utilize it to improve the accuracy of ranking results. SSRM supports a personalized and trusted selection of cloud services through taking into account mobile user's trust expectation. Simulation experiments are conducted on ns3 simulator to study the prediction performance of SSRM compared with other two traditional approaches. The experimental results show the effectiveness of SSRM.

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SPASC: Strategy‐proof auction mechanism with cost and QoS incentive for service composition

Yao

Huang

Zhang

et al. 2021

Concurrency and Computation

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In cloud computing, service composition provides an appealing paradigm to compose existing single services into a powerful workflow. The critical issues in service composition is that service providers may lie about the service's cost and QoS for more profits. However, existing studies only address the issue of services' untruthful cost, none of the studies consider both the issue of untruthful cost and untruthful QoS. Thus, it is necessary to design an incentive mechanism to motivate service providers to offer services with the truthful cost and truthful QoS. To address the above challenging issues, we propose the strategy‐proof auction mechanism for service composition (SPASC), which can determine the winners and dynamically calculate the price of services. To the best of our knowledge, this is the first study to design an incentive mechanism to motivate service providers to offer services with the truthful cost and truthful QoS in this field. Furthermore, we propose an auction algorithm to implement the SPASC. Both theoretical proof and the extensively experimental results validate that SPASC achieves the truthfulness and individual rationality. At the same time, the experimental results also prove that this mechanism can motivate service providers to offer services with the truthful cost and truthful QoS.

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Toward trustworthy cloud service selection: A time-aware approach using interval neutrosophic set

Cited by 69 publications

References 53 publications

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Context‐Aware Cloud Service Selection Model for Mobile Cloud Computing Environments

SPASC: Strategy‐proof auction mechanism with cost and QoS incentive for service composition

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