Open AccessSelecting Cloud Deployment Model Using a Delphi Analytic Hierarchy Process (DAHP)
Author(s) -
James Ngeru,
Tridip Kumar Bardhan
Publication year - 2015
Publication title -
industrial and systems engineering review
Language(s) - English
Resource type - Journals
ISSN - 2329-0188
DOI - 10.37266/iser.2015v3i1.pp59-70
Subject(s) - cloud computing , software deployment , analytic hierarchy process , computer science , flexibility (engineering) , process management , process (computing) , delphi method , provisioning , business model , service (business) , knowledge management , business , operations research , engineering , marketing , artificial intelligence , software engineering , telecommunications , statistics , mathematics , operating system
Cloud computing is a significant paradigm shift in information technology (IT) service offerings that has been receiving enormous attention in academic and IT industry. Recent years has seen exponential growth in cloud use adoption, where many organizations are moving their IT resources into cloud due to flexibility and low-cost. However, on account of rapid innovation and growth in cloud technologies and service providers, selecting the right cloud services, provider and strategy is becoming increasing a common challenge to organization during cloud adoption. In an attempt to address this challenge, we propose application of Delphi Analytic Hierarchy Process (DAHP) method in selecting cloud deployment model. There are several cloud deployment models and organizations must identify the right model that best suits their business needs. The proposed approach facilitates a collaborative decision making process, consisting a number of decision makers whom, with consensus facilitated by the DAHP process, identifies feasible approaches, decision making factors and ultimate selection of a cloud deployment model alternative that is based on organizational business needs and capabilities. The DAHP process is illustrated by a means of a case study. The DAHP result analysis, as was illustrated in the case study, helps in explaining and justifying the choice selected as the best cloud deployment model.