Multi-cloud Services Configuration Based on Risk Optimization

Nowadays risk analysis becomes critical in the Cloud Computing domain due to the increasing number of threats affecting applications running on cloud infrastructures. Multi-cloud environments allow connecting and migrating services from multiple cloud providers to manage risks. This paper addresses the question of how to model and configure multi-cloud services that can adapt to changes in user preferences and threats on individual and composite services. We propose an approach that combines Product Line (PL) and Machine Learning (ML) techniques to model and timely find optimal configurations of large adaptive systems such as multi-cloud services. A three-layer variability modeling on domain, user preferences, and adaptation constraints is proposed to configure multi-cloud solutions. ML regression algorithms are used to quantify the risk of resulting configurations by analyzing how a service was affected by incremental threats over time. An experimental evaluation on a real life electronic identification and trust multi-cloud service shows the applicability of the proposed approach to predict the risk for alternative re-configurations on autonomous and decentralized services that continuously change their availability and provision attributes.