QoS based Web Service Selection and Multi-Criteria Decision Making Methods

TitleQoS based Web Service Selection and Multi-Criteria Decision Making Methods
Publication TypeJournal Article
Year of PublicationIn Press
AuthorsBagga, P., A. Joshi, and R. Hans
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueIn Press
VolumeIn Press
NumberIn Press
Date Published12/2017
Pagination1-10
Abstract

With the continuing proliferation of web services offering similar efficacies, around the globe, it has become a challenge for a user to select the best web service. In literature, this challenge is exhibited as a 0-1 knapsack problem of multiple dimensions and multiple choices, known as an NP-hard problem. Multi-Criteria Decision Making (MCDM) method is one of the ways which suits this problem and helps the users to select the best service based on his/her preferences. In this regard, this paper assists the researchers in two conducts: Firstly, to witness the performance of different MCDM methods for large number of alternatives and attributes. Secondly, to perceive the possible deviation in the ranking obtained from these methods. For carrying out the experimental evaluation, in this paper, five different well-known MCDM methods have been implemented and compared over two different scenarios of 50 as well as 100 web services, where their ranking is defined on an account of several Quality of Service (QoS) parameters. Additionally, a Spearman’s Rank Correlation Coefficient has been calculated for different pairs of MCDM methods in order to provide a clear depiction of MCDM methods showing the least deviation in their ranking. The experimental results comfort web service users in conquering an appropriate decision on the selection of suitable service.

KeywordsAHP, COPRAS, SAW, Spearman’s Rank Correlation Coefficient, TOPSIS, VIKOR
DOI10.9781/ijimai.2017.12.001
URLhttp://www.ijimai.org/journal/sites/default/files/files/2017/12/ip_12_01_pdf_11299.pdf
AttachmentSize
ip_12_01.pdf1.06 MB