Probabilistic Model Checking at Runtime for the Provisioning of Cloud Resources

Abstract

We elaborate on the ingredients of a model-driven approach for the dynamic provisioning of cloud resources in an autonomic manner. Our solution has been experimentally evaluated using a NoSQL database cluster running on a cloud infrastructure. In contrast to other techniques, which work on a best-effort basis, we can provide probabilistic guarantees for the provision of sufficient resources. Our approach is based on the probabilistic model checking of Markov Decision Processes (MDPs) at runtime. We present: (i) the specification of an appropriate MDP model for the provisioning of cloud resources, (ii) the generation of a parametric model with system-specific parameters, (iii) the dynamic instantiation of MDPs at runtime based on logged and current measurements and (iv) their verification using the PRISM model checker for the provisioning/deprovisioning of cloud resources to meet the set goals (This research has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning of the National Strategic Reference Framework (NSRF) - Research Funding Program: Thales. Investing in knowledge society through the European Social Fund.”).