A Petri net decision model for cloud services adoption

• Autor(a) principal: Maristella Ribas
• Data de Publicação: 2015
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFC
• Texto Completo: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=15609
• topic: ComputaÃÃo em nuvem; ServiÃos em nuvem; Redes de Petri; Cloud computing; Petri nets; Business Driven IT Management (BDIM); Analytical Hierarquical Process (AHP); Spot Instances; ENGENHARIAS

Cloud services are now widely used, especially in Infrastructure as a Service (IaaS), with big players offering several purchasing options, and expanding almost daily the range of offered services. Cost reduction is a major factor promoting cloud services adoption. However, qualitative factors need to be evaluated as well, making the decision process of cloud services adoption a non-trivial task for managers. In this work, we propose a Petri net-based multi-criteria decision-making (MCDM) framework, in order to evaluate a cloud service in relation to a similar on-premises offer. The evaluation of both options considers cost and qualitative issues in a novel and simple method that incorporates best practices from academy and IT specialists. Furthermore, the use of Petri net models allows powerful extensions to perform deeper analysis of specific factors as needed. The framework can be helpful for IT managers to decide between the two options, and can be used for any type of cloud service (IaaS, SaaS, PaaS). Since cost is one of the most important factors promoting cloud adoption, we proceed with a deeper analysis of one important cost factor. We propose a Petri net to model cost savings using public clouds spot Instances purchasing option. Through extensive simulations in several scenarios we conclude that spot Instances can be a very interesting option for savings in auto scaling process, even in simple business applications using only a few servers. Exploring different purchasing options for cloud services can make the difference in the decision making process.