Location‐Aware Web Service Composition Based on the Mixture Rank of Web Services and Web Service Requests

Lu, Junwen; Liu, Guanfeng; Wu, Kejian; Qin, Wenjiang

doi:10.1155/2019/9871971

Cited by 9 publications

(7 citation statements)

References 31 publications

(73 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…and it is capable of using the learned deep latent features of neighbors to derive the features of a user or a service. An approach for solving the service composition problem was proposed in [39] for the case of having two types of requirements of QoS. The authors proposed a composition method and they gave an analysis from composition models of services and from the related quality of experience of web services.…”

Section: Service Recommender Systemmentioning

confidence: 99%

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

Al-Zanbouri¹

2021

Preprint

View full text Add to dashboard Cite

Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.

show abstract

Section: Service Recommender Systemmentioning

confidence: 99%

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

Al-Zanbouri¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…C. Zhang [27] used a particle swarm optimization algorithm for service composition that considered multiple feasible service composition candidates. J. Lu et al [28] sorted services and service requests and tried to find the optimal mapping based on ranking. Considering Quality of Service (QoS), S. K. Gavvala et al [29] used Whale Optimization Algorithm to provide users' optimal values on QoS.…”

Section: Related Workmentioning

confidence: 99%

“…According to prior research, there are four kinds of service structures (Fig. 2): sequence structure, cycle structure, parallel structure, and case structure [28]. In this section, we will give the distance and time in details.…”

Section: A Analysis Of Various Structures Of Service Compositionmentioning

confidence: 99%

An Energy-Aware Multi-Target Service Composition Method in a Multi-Cloud Environment

Yin

Hao

2020

IEEE Access

View full text Add to dashboard Cite

Service composition is always employed to enhance function by composing atomic services that reside in different clouds together. In a multi-cloud environment (MCE), the time and energy consumption of service composition may differ because of the dynamic of the network. The network varies between clouds, or even between composite requests and clouds. A cloud provides a limited variety of services, which makes a composite request that requires multiple clouds to jointly compose all atomic services together. All of this makes it more difficult than ever to implement energy-aware service composite in a MCE. In this paper, we model service composition in the MCE and propose an energy-aware multiple targets service composition method for executing atomic services in a composite request. Since the proposed method gets the scheduling by searching all possible mappings between service request blocks (a set of requests to multiple atomic services) and clouds, we call it "All-Search". To reduce the complexity of All-Search, we propose "Itersplit", a heuristics algorithm that can achieve multiple targets. The performance of All-Search, Cloud-SEnergy, and Itersplit is tested through simulations. The results in multiple aspects indicate that Itersplit performs better than other algorithms when we take Itersplit-20%.

show abstract

“…The process of creating composite services can be performed iteratively, with one composite service serving as a component of a more complicated composite service. The true power of Web services, as demonstrated above, is their ability to be dynamically integrated into composite services, performing new tasks and conducting new business transactions [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

2022

Entropy

View full text Add to dashboard Cite

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.

show abstract

Location‐Aware Web Service Composition Based on the Mixture Rank of Web Services and Web Service Requests

Cited by 9 publications

References 31 publications

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

An Energy-Aware Multi-Target Service Composition Method in a Multi-Cloud Environment

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

Contact Info

Product

Resources

About