Specification and Quantitative Analysis of Probabilistic Cloud Deployment Patterns

Cloud computing is a new technological paradigm offering computing infrastructure, software and platforms as a pay-as-you-go subscription based service. Many potential customer’s of cloud services require essential cost assessments to be undertaken before transitioning to the cloud. Current assessment techniques are imprecise and rely on simplified resource requirements that fail to account for probabilistic variations in usage. In this paper, we address these problems and propose a new probabilistic pattern modelling (PPM) approach to cloud costing and resource usage assessment. Our approach is based on a concise expression of probabilistic resource usage patterns translated to Markov decision processes (MDPs). Key costing and usage queries are identified and expressed in a probabilistic variation of temporal logic and calculated to a high degree of precision using quantitative verification techniques. The PPM cost assessment approach has been implemented as a Java library and validated with a case study and scalability experiments.