Web service quality of service prediction via regional <scp>reputation‐based</scp> matrix factorization

Ghafouri, Seyyed Hamid; Hashemi, Seyyed Mohsen; Razzazi, Mohammadreza; Movaghar, Ali

doi:10.1002/cpe.6318

Cited by 7 publications

(6 citation statements)

References 57 publications

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“…The QoS determines the performance of a Web service [ 22 ], so the QoS is regarded as the most important non-functional criterion for service selection and composition [ 23 , 24 , 25 ]. However, the QoS that a given Web service will supply is not predictable and foreseeable due to dynamic changes in network performance [ 3 , 8 , 17 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive and Unified Approach to Web Service Trust Evaluation Based on Uncertainty Methodology

Zhang

2022

Entropy

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Web services have the advantage of being able to generate new value-added services based on existing services. To effectively compose Web services, the composition process necessitates that the services that will participate in a given composite service are more trustworthy than those that provide similar functionality. The trust mechanism appears to be a promising way for determining service selection and composition. Existing trust evaluation approaches do not take into account customer expectations. Based on fuzzy set theory and probability theory, this work proposes a unique Web service trust evaluation approach that is notable for its ability to provide personalized service selection based on customer expectations and preferences. The proposed approach defines trust as a fuzzy notion that is related to prior experiences and ratings, and expresses trust in two different forms. This work mainly solves two key issues in Web service trust architectures, bootstrapping trust for the newcomer services and deriving trust for composite services. The proposed approach combines the solutions to numerous issues in a natural way. The case study and approaches comparison demonstrate that the proposed approach is feasible.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive and Unified Approach to Web Service Trust Evaluation Based on Uncertainty Methodology

Zhang

2022

Entropy

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“…In our previous study, we introduced a new type of reputation called regional reputation and based on that, a new QoS prediction method was presented. 1 However, all these methods combine the concept of the user's reputation with a specific prediction method to provide a reliable prediction method.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the new technologies or paradigms such as service oriented architecture (SOA), cloud computing, and the Internet of Things (IoT) are based on web services. As a result, web services have a prominent role in software development and software engineering 1 . Nowadays, a large number of studies on different web service related challenges such as service discovery, 2–4 service composition, 5–8 service ranking, 9–11 and service recommendation 12–14 have been done.…”

Section: Introductionmentioning

confidence: 99%

“…They have combined the user's reputations with another method such as Dirichlet distribution or prepared a new type of reputation in their works. In our previous study, we introduced a new type of reputation called regional reputation and based on that, a new QoS prediction method was presented 1 . However, all these methods combine the concept of the user's reputation with a specific prediction method to provide a reliable prediction method.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, web services have a prominent role in software development and software engineering. 1 Nowadays, a large number of studies on different web service related challenges such as service discovery, [2][3][4] service composition, [5][6][7][8] service ranking, [9][10][11] and service recommendation [12][13][14] have been done. In all the above challenges, selecting the appropriate web services among the huge set of candidate web services is important.…”

Section: Introductionmentioning

confidence: 99%

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A new model for trustworthy web service QoS prediction

Hashemi

Ghafouri

Hung

2021

Concurrency and Computation

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The number of web services available on the internet has exploded, and as a result, the number of services with the same functionality has exploded as well. Therefore, selecting the best web service from functionally similar services is a critical task in the web service domain. The Quality of Service (QoS) is one of the most common criteria used to select the best web service. Collaborative filtering (CF) has been utilized in several studies to predict the values of QoS attributes of web services for each user in a personalized way. The QoS histories of other users are employed in these methods to predict the QoS values of the active user. Although these methods function well and produce acceptable prediction results, the accuracy of their predictions can be harmed by incorrect data provided by untrustworthy users. In this study, we propose a new model that reduces the impact of unreliable user data, resulting in a trustworthy prediction. This model can be applied to any existing prediction method. In experiments, the proposed model was applied to seven known prediction methods. The results indicate that this model is able to eliminate the impact of unreliable users.

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QoS prediction of cloud services by selective ensemble learning on prefilling‐based matrix factorizations

Mao,

Chen,

Towey

et al. 2024

Concurrency and Computation

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SummaryWhen selecting services from a cloud center to build applications, the quality of service (QoS) is an important nonfunctional attribute to be considered. However, in actual application scenarios, the QoS details for many services may not be available. This has led to a situation where prediction of the missing QoS records for services has become a key problem for service selection. This article presents a selective ensemble learning (SEL) framework for prefilling‐based matrix factorization (PFMF) predictors. In each PFMF predictor, the improved collaborative filtering is defined by examining the stability of the QoS records when measuring the similarity of users (or services), and then used to prefill empty records in the initial QoS matrix. To ensure the diversity of the basic PFMF predictors, various prefilled QoS matrices are constructed for the matrix factorization. In this process, different reference weights are assigned to the original and the prefilled QoS records. Finally, particle swarm optimization is used to set the ensemble weights for the basic PFMF predictors. The proposed SEL on PFMF (SEL‐PFMF) algorithm is validated on a public dataset, where its prediction performance outperforms the state‐of‐the‐art algorithms, and also shows good stability.

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Web service quality of service prediction via regional reputation‐based matrix factorization

Cited by 7 publications

References 57 publications

A Comprehensive and Unified Approach to Web Service Trust Evaluation Based on Uncertainty Methodology

A Comprehensive and Unified Approach to Web Service Trust Evaluation Based on Uncertainty Methodology

A new model for trustworthy web service QoS prediction

QoS prediction of cloud services by selective ensemble learning on prefilling‐based matrix factorizations

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