Title:Intuitionistic Fuzzy Score Function Based Multi-Criteria Decision Making Method for Selection of Cloud Service Provider
VOLUME: 10 ISSUE: 4
Author(s):Sonal Agrawal* and Pradeep Tripathi
Affiliation:Department of Computer Science and Engineering, Vindhya Institute of Technology & Science, Satna-485001 (MP), Department of Computer Science and Engineering, Vindhya Institute of Technology & Science, Satna-485001 (MP)
Keywords:Cloud computing, intuitionistic fuzzy sets, MCDM, score function, multi-criteria decision-making, multi-objective problems.
Abstract:
Aims & Background: Cloud Computing (CC) has received great attention from the
scholarly researchers and IT companies. CC is a standard that offers services through the Internet.
The standard has been manipulated by existing skills (such as collect, peer-to-peer and grid computing)
and currently accepted by approximately all major associations. Various associations like
as Microsoft and Facebook have revealed momentous investments in CC and currently offer services
with top levels of reliability. The well-organized and precise evaluation of cloud-based
communication network is an essential step in assurance both the business constancy and the continuous
open services.
Objectives & Methods: To select and rank the CC service providers, we introduce an Improved
Score Function (ISF) based Multi-Criteria Decision-Making (MCDM) approach. The proposed
approach is developed to solve the MCDM problems with partly unknown weight. To do this, the
criteria preferences are given in terms of Intuitionistic Fuzzy Sets (IFSs). Numerical example is
illustrated to show the effectiveness of the proposed approach over the previous ones.
Results: A decision making problem of cloud computing service provider has been considered for
signifying the developed technique and finishes with the outcomes coincide with the already developed
methods which confirms the solidity of the developed method.
Conclusion: For future, we plan to implement the proposed technique on various decision making
problems, clustering and multi-objective problems. Also, we plan to extend our method under different
uncertain atmosphere by using other MCDM methods.
