Cloud Service Provider Selection Using Fuzzy Data Envelopment Analysis Based on SMI Attributes

Introduction

Cloud computing uses virtualization and the modern web to provide diverse services, in a reliable and scalable way, to multiple consumers, as and when required. Cloud computing provides an excellent opportunity for new business ideas and existing businesses to transform. Consumers can directly use the cloud services like computing power, storage or use it to host their services or applications. The major difficulty faced by the customers is the selection of appropriate cloud provider (also called as cloud service provider) from the large pool of providers in the market. Cloud computing solutions have gained popularity as businesses became more aware of its benefits and services. A cloud broker is a third-party independent contractor that works as a middleman among a cloud technology buyer as well as a cloud service vendor. A brokerage is a person who works as a go-between for two or more entities throughout a transaction. A wide range of cloud services with similar features are available to the cloud end users. The end users of the cloud could be Business Establishments or Cloud Brokers. This study creates a framework to select an appropriate cloud service provider as per the end user’s need. The researchers in the literature have proposed many ranking and selection approaches for finding the best suitable cloud provider from the available list. Multi-Criteria Decision-Making (MCDM) refers to approaches for generating informed judgments where numerous criteria must be examined simultaneously in addition to determining else providing with options. It is sometimes based on a multi-decision analysis (MCDA). TOPSIS, fuzzy decision-making, analytical hierarchy process (AHP), data envelopment analysis (DEA) besides analytical network process (ANP) are some of the MCDM methodologies accessible. In several fields, MCDM has been among the quickest increasing issue categories. The most effective techniques are MCDM methods like Analytical Hierarchical Process (AHP), Technique for order preference by similarity to an ideal solution (TOPSIS) etc. The Quality of Service (QoS) factors play a significant role in the decision-making. There is a need for better MCDM approach that satisfies the customer’s QoS requirements. NAHP called as Neutrosophic AHP was proposed by (Alhanahnah et. al., 2018) for solving the cloud service selection problem. The fundamental drawback of AHP is that it is unable to capture the ambiguity of human ideas. The classic AHP technique analyses decision-makers’ specific judgments. The neutrosophic set concept, on the other hand, makes professional judgments increasingly adaptable.