Towards A Cross-Domain MapReduce Framework
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Authors
Nguyen, Thuy D.
Gondree, Mark A.
Khosalim, Jean
Irvine, Cynthia E.
Subjects
MapReduce
Hadoop
cross-domain services
multilevel security
Hadoop
cross-domain services
multilevel security
Advisors
Date of Issue
2013
Date
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Abstract
The Apache™ Hadoop® 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. 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. 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. 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.
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Article
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Computer Science (CS)
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.