IntegrityMR: Exploring Result Integrity Assurance Solutions for Big Data Computing Applications

Abstract

Large-scale adoption of MapReduce applications on public clouds is hindered by the lack of trust on the participating virtual machines deployed on the public cloud. In this paper, we propose IntegrityMR, a multi-public clouds architecture-based solution, which performs the MapReduce-based result integrity check techniques at two alternative layers: the task layer and the application layer. Our experimental results show that solutions in both layers offer a high result integrity but non-negligible performance overheads.