Qos prediction for web services based on Restricted Boltzmann Machines

Thinh, Le Van

doi:10.1007/s12927-017-0010-6

Cited by 10 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A deep neural network model has also been used for QoS prediction. In [57], the authors propose a two-layer model for prediction of quality web services. It uses Restricted Boltzmann…”

Section: Qos Prediction and Service Recommendationmentioning

confidence: 99%

Machine Learning Service Recommendation Based on Accuracy Related QoS Attributes

Alghofaily

2023

Preprint

View full text Add to dashboard Cite

<p>Machine Learning (ML) is a topic of interest in computer science. Amazon AWS, Google Cloud and Microsoft Azure are examples of cloud platforms that provide ML services. Analysts can easily analyze data using online ML services, and most of the model processing is done using cloud services to provide fast and reliable results. Many of these services offer similar functionalities, and the challenge is how to select the best service for the data. Traditional approaches for service-selection depend only on general understanding of the service functionality. A better way is to consider both functional and non-functional requirements. Functional requirements determine overall behaviour of a service. Non-functional requirements establish how relevant a service is to the user’s query and refer to the Quality of Service (QoS) attribute. QoS- based service-selection has been studied in the service computing community for some time. However, characteristics of the input dataset are not usually considered in the selection process even though they might affect the QoS values of the service. In this dissertation, we investigate the impact of adding dataset features and other side information on the performance of QoS prediction and service recommendation. We focus on ML services since their QoS values are potentially highly dependent on the dataset.</p> <p>We propose two approaches for ML service recommendation and compare their performances. The first approach uses factorization for web service recommendation. We identify latent features of the datasets and the services and then recommend services by exploiting these latent variables. The second approach uses neural networks to identify latent features. We also integrate two sets of side information (dataset and service) in both approaches, and study the effect of these added features. In the experiment, we test our system using the real QoS data of 24 classification models running on 390 datasets downloaded from OpenML. In both implementations, models with side information outperform the basic model. To guarantee the best performance for our model, adding side information is necessary, which increases the predictive accuracy by 5% to 25%. Thus, we recommend integrating the side information in recommender systems, specifically including dataset features when recommending ML services. </p>

show abstract

“…A deep neural network model has also been used for QoS prediction. In [57], the authors propose a two-layer model for prediction of quality web services. It uses Restricted Boltzmann…”

Section: Qos Prediction and Service Recommendationmentioning

confidence: 99%

Machine Learning Service Recommendation Based on Accuracy Related QoS Attributes

Alghofaily

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…QoS prediction by using a large dataset is another issue in the web services selection and recommendation field. Thinh [57] proposed a 'two-layer model' (TLM) to evaluate the prediction of quality web services. The evaluation of the proposed TLM was performed on the dataset [28], [29].…”

Section: Related Workmentioning

confidence: 99%

Benchmark Dataset Selection of Web Services Technologies: A Factor Analysis

et al. 2020

View full text Add to dashboard Cite

Web services have emerged as an accessible technology with the standard 'Extensible Mark Up' (XML) language, which is known as 'Web Services Description Language' WSDL. Web services have become a promising technology to promote the interrelationship between service providers and users. Web services users' trust is measured by quality metrics. Web service quality metrics vary in many benchmark datasets used in the existing studies. The selection of a benchmark dataset is problematic to classify and retest web services. This paper proposes a method to rank web services quality metrics for the selection of benchmark web services datasets. To measure the diversity in quality metrics, factor analysis with Varimax rotation and scree plot is a well-established method. We use factor analysis to determine percentage variance among principal factors of four benchmark datasets. Our results showed that the two-factor solution explained 94.501, 76.524, and 45.009% variances in datasets A, B, and D, respectively. A three-factor solution explained 85.085% variance in dataset C. Reliability, and response time quality metrics were predicted as the most dominating quality metrics that contributed to explain the percentage variance in four datasets. Our proposed web metric ranking (WMR) method resulted in reliability as the topmost web metric with (57.62%) score and latency web metric at the bottom-most with (3.60%) score. The proposed WMR method showed a high (96.17%) ranking precision. Obtained results verified that factor solutions after reducing the dimensions could be generalized and used in the quality improvement of web services. In future works, the authors plan to focus on a dataset with dominating quality metrics to perform regression testing of web services. INDEX TERMS Factor analysis, quality metrics, rotated loading, reliability, response time, regression testing, web services.

show abstract