Qos-Based Web Service Discovery And Selection Using Machine Learning
DOI:
https://doi.org/10.4108/eai.29-5-2018.154809Keywords:
Web Service, WSDL, QoS prediction, Machine learning, Service Provider reputationAbstract
In service computing, the same target functions can be achieved by multiple Web services from di˙erent providers. Due to the functional similarities, the client needs to consider the non-functional criteria. However, Quality of Service provided by the developers su˙ers scarcity and lack of reliability. In addition, the reputation of the service providers is an important factor, especially those with little experience, to select a service. Most of the previous studies were focused on the user's feedbacks for justifying the selection. Unfortunately, not all the users provide the feedback unless they had extremely good or bad experience with the service. In this vision paper, we propose a novel architecture for the web service discovery and selection. The core component is a machine learning based methodology to predict the QoS properties using source code metrics. The credibility value and previous usage count are used to determine the reputation of the service.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 EAI Endorsed Transactions on Scalable Information Systems
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.