POSTER: RIA: an Audition-based Method to Protect the Runtime Integrity of MapReduce Applications

Abstract

Public cloud vendors have been offering varies big data computing services. However, runtime integrity is one of the major concerns that hinders the adoption of those services. In this paper, we focus on MapReduce, a popular big data computing framework, propose the runtime integrity audition (RIA), a solution to verify the runtime integrity of MapReduce applications. Based on the idea of RIA, we developed a prototype system, called MR Auditor, and tested its applicability and the performance with multiple Hadoop applications. Our experimental results showed that MR Auditor is an efficient tool to detect runtime integrity violation and incurs a moderate performance overhead.