TITLE:
Cloudysme: An Ontological Framework for Aiding SMEs Adoption of SaaS in a Cloud Environment
AUTHORS:
Richard Ikechukwu Otuka, Abdel-Rahman Tawil, Ameer Al-Nemrat
KEYWORDS:
SaaS, Ontology, AHP, DSS, SME, Service Ranking, Knowledge Management
JOURNAL NAME:
Journal of Computer and Communications,
Vol.5 No.14,
December
29,
2017
ABSTRACT: Adoption techniques are widely applied in and for cloud service usage to improve the slow acceptance rate of cloud services by SMEs. In such context, a well-understood problem is finding a suitable service from the vast number of services offering similar packages to satisfy user requirements such as security, cost, trust and operating systems compatibility has become a big challenge. However, a major drawback of existing techniques such as frameworks, web search, decision support tools, management models, ontology models and agent technology is that they are restricted to a specific task or they replicate service provider offerings. In this paper, we present Cloudysme a cloud service adoption solution, a middleware that is capable of aiding the decision making process for SMEs adoption of cloud services. Using a case study of SaaS storage services offerings by cloud providers, we introduce a new formalism for judging the superiority of one service attribute over another, we propose an extended version of pairwise comparison and Analytical hierarchical Process (AHP) which is a traditional multi-criteria decision method (MCDM) in solving complex comparisons. We solve the issue of service recommendation by introducing an acceptable standard for each service attribute and propose a protocol using rational relationships for aiding cloud service ranking process. We tackle the issue of specific tasking by using a set of concepts and associated semantic rules to rank and retrieve user requirements. We promote a knowledge engineering approach for natural language processing by using terms and conditions in translating human sentences to machine readable language. Finally, we implement our system using 30 SMEs as a pivotal study. We prove that the use of semantic rules within an ontology can tackle the issue of specific tasking.