Quality of service (QoS) parameters prediction for web services using hybrid neural network and ensemble methods

Lov Kumar () and Ashish Sureka ()
Additional contact information
Lov Kumar: BITS Pilani
Ashish Sureka: Ashoka University

International Journal of System Assurance Engineering and Management, 2019, vol. 10, issue 6, No 15, 1577-1614

Abstract: Abstract Web services are an essential part of the everyday operation for a number of organizations. Applications within organizations frequently depend on web services to fulfill customers needs. Failed and slow online application depending on web services can cost a lot to organizations in terms of customer dissatisfaction. Estimation and improvement of the quality of service (QoS) parameters of web service serve as the primary objective of service providers. We hypothesize that analyzing the source code behind the web services helps in computing the QoS parameters. In this paper, we analyze the source code using thirty-seven different source code metrics (SCMs) to measure the internal structure of the software system. In order to develop a model, these SCMs are treated as input for predicting QoS parameters using nine different prediction techniques and three different ensemble methods. Since SCM act as an important parameter for assessing the performance of prediction techniques, hence for dimensionality reduction and removing irrelevant features, four distinct feature reduction techniques are considered in the study during implementation. Two distinct evaluation criteria such as MMRE and RMSE, are considered for evaluating and comparing the performance of the developed QoS parameters prediction models. The experimental results reveal that the weighted-based ensemble method produces better results. The results also depict that the ME, Ca, IC, DAM, and MFA metrics are commonly found relevant metrics for QoS parameters prediction. Furthermore, we also observe that the selected set of metrics achieves better results compared to all metrics.

Keywords: Web services; Service oriented computing; Ensemble methods; Feature selection technique; Neural network; Gradient descent algorithm; Genetic algorithm; Quality of service (QoS) parameters (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-019-00911-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:10:y:2019:i:6:d:10.1007_s13198-019-00911-9

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-019-00911-9

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().