Towards A Cross-Domain MapReduce Framework

The Apache (TM) Hadoop (R) framework provides parallel processing and distributed data storage capabilities that data analytics applications can utilize to process massive sets of raw data. These Big Data applications typically run as a set of MapReduce jobs to take advantage of Hadoop's ease of service deployment and large-scale parallelism. Yet, Hadoop has not been adapted for multilevel secure (MLS) environments where data of different security classifications co-exist. To solve this problem, we have used the Security Enhanced Linux (SELinux) Linux kernel extension in a prototype cross-domain Hadoop on which multiple instances of Hadoop applications run at different sensitivity levels. Their accesses to Hadoop resources are constrained by the underlying MLS policy enforcement mechanism. A benefit of our prototype is its extension of the Hadoop Distributed File System to provide a cross-domain read-down capability for Hadoop applications without requiring complex Hadoop server components to be trustworthy.