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 va...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.展开更多
文摘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.
