@Article{cmes.2020.08854,
AUTHOR = {Alireza Poordavoodi, Mohammad Reza Moazami Goudarzi, Hamid Haj Seyyed Javadi, Amir Masoud Rahmani, Mohammad Izadikhah},
TITLE = {Toward a More Accurate Web Service Selection Using Modified Interval DEA Models with Undesirable Outputs},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {123},
YEAR = {2020},
NUMBER = {2},
PAGES = {525--570},
URL = {http://www.techscience.com/CMES/v123n2/38690},
ISSN = {1526-1506},
ABSTRACT = {With the growing number of Web services on the internet, there is a challenge to
select the best Web service which can offer more quality-of-service (QoS) values at the
lowest price. Another challenge is the uncertainty of QoS values over time due to the
unpredictable nature of the internet. In this paper, we modify the interval data envelopment
analysis (DEA) models [Wang, Greatbanks and Yang (2005)] for QoS-aware Web service
selection considering the uncertainty of QoS attributes in the presence of desirable and
undesirable factors. We conduct a set of experiments using a synthesized dataset to show the
capabilities of the proposed models. The experimental results show that the correlation
between the proposed models and the interval DEA models is significant. Also, the
proposed models provide almost robust results and represent more stable behavior than the
interval DEA models against QoS variations. Finally, we demonstrate the usefulness of the
proposed models for QoS-aware Web service composition. Experimental results indicate
that the proposed models significantly improve the fitness of the resultant compositions when
they filter out unsatisfactory candidate services for each abstract service in the
preprocessing phase. These models help users to select the best possible cloud service
considering the dynamic internet environment and they help service providers to
improve their Web services in the market.},
DOI = {10.32604/cmes.2020.08854}
}