An MCDM method for cloud service selection using a Markov chain and the best-worst method
Document Type
Journal Article
Publication Title
Knowledge-Based Systems
ISSN
09507051
Volume
159
First Page
120
Last Page
131
Publisher
Elsevier BV
School
School of Science
RAS ID
26944
Abstract
Due to the increasing number of cloud services, service selection has become a challenging decision for many organisations. It is even more complicated when cloud users change their preferences based on the requirements and the level of satisfaction of the experienced service. The purpose of this paper is to overcome this drawback and develop a cloud broker architecture for cloud service selection by finding a pattern of the changing priorities of User Preferences (UPs). To do that, a Markov chain is employed to find the pattern. The pattern is then connected to the Quality of Service (QoS) for the available services. A recently proposed Multi Criteria Decision Making (MCDM) method, Best Worst Method (BWM), is used to rank the services. We show that the method outperforms the Analytic Hierarchy Process (AHP). The proposed methodology provides a prioritized list of the services based on the pattern of changing UPs. The methodology is validated through a case study using real QoS performance data of Amazon Elastic Compute (Amazon EC2) cloud services.
DOI
10.1016/j.knosys.2018.06.010
Access Rights
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Comments
Nawaz, F., Asadabadi, M. R., Janjua, N. K., Hussain, O. K., Chang, E., & Saberi, M. (2018). An MCDM method for cloud service selection using a markov chain and the best-worst method. Knowledge-Based Systems, 159, 120-131. Available here