Risk-Aware Workload Distribution in Hybrid Clouds

This paper explores an efficient and secure mechanism to partition computations across public and private machines in a hybrid cloud setting. We propose a principled framework for distributing data and processing in a hybrid cloud that meets the conflicting goals of performance, sensitive data disclosure risk and resource allocation costs. The proposed solution is implemented as an add-on tool for a Hadoop and Hive based cloud computing infrastructure. Our experiments demonstrate that the developed mechanism can lead to a major performance gain by exploiting both the hybrid cloud components without violating any pre-determined public cloud usage constraints.