Selection of Cloud Delivery and Deployment Models: An Expert System Approach

Introduction

Recent statistics on cloud computing indicates a steady growth in adoption and technological development. A very recent survey, of 1002 IT professionals, on cloud computing trends reveals that 85 percent of enterprises represented by respondents have multi-cloud strategy up from 82 percent in 2016. Private cloud adoption reached 72 percent, and the hybrid cloud adoption reached 67 percent (Weins, 2017). More awareness of cloud computing capabilities leads to higher cloud adoption (Tashkandi & Al-Jabri, 2015; Al-Jabri & Alabdulhadi, 2016). Cloud computing represents a shift of computing paradigm towards outsourcing of computing services with the flexibility and scalability, the need for low budget, and measured services (Catteddu & Hogben, 2009; Azadegan &Teich, 2010). For many businesses, cloud computing represents an attractive option in terms of cost saving in establishing and maintaining IT resources.

The cloud computing industry offers businesses different adoption scenarios. Businesses face a number of challenging decisions with respect to adopting cloud computing. For example, one of the challenges is the selection of a cloud service delivery model, from alternate options like SaaS, PaaS, or IaaS. Further, another challenging decision is the choice of deployment model amongst the common models (Private, Public, Hybrid, or Community). This decision will involve considering organizational factors, business information needs, and budget requirements. These challenging decisions require due consideration of several factors, which in turn generate multiple decision scenarios simultaneously. A review of existing literature revealed a lack of research work in addressing the choice of cloud service delivery and deployment models pertaining to managerial decision making of cloud computing adoption. Thus, the objective of this paper is to fill the research gap by proposing an expert system prototype and evaluate its effectiveness to support management in addressing these twin issues of selection decisions for managing the adoption of cloud computing.

The rest of the paper is organized in the following manner. A literature review on cloud computing is given. It focused on cloud computing technology, cloud computing models and the characteristics of service delivery and deployment models. In, addition, the review provides an overview of the relevant decision-making approaches including expert system and multi-criteria decision analysis. Then, the research method follows, which explains how the authors address the study objectives, by proposing an expert system. Next, the paper evaluates the proposed expert system effectiveness. Then, the paper discusses the research results. Lastly, the conclusion summarizes the study and addresses its limitations. This paper makes a significant contribution to existing literature. Being the first paper of its kind to propose the expert system approach, this research will provide new insights into and extend our understanding of managing cloud-computing adoption.