Hadoop MapReduce for Tactical Clouds
Xie, Geoffrey G.
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We envision a future where real-time computation on the battlefield provides the tactical advantage to an Army over its adversary. The ability to collect and process large amounts of data to provide actionable information to soldiers will greatly enhance their situational awareness. Our vision is based on the observation that the U.S. Military is attempting to equip soldiers with smartphones. While individual phones may not be sufficiently powerful for processing large amount of data, using the mobile devices carried by a squad or platoon of Soldiers as a single distributed computing platform, a Tactical Cloud, would enable large-scale data processing to be conducted in battlefields. In order for this vision to be realized, two issues have to be addressed. The first is the complexity of writing applications for distributed computing environments, and the second is the vulnerability of data on mobile devices. In this paper, we propose combining two existing technologies to address these issues. The first is Hadoop MapReduce, a scalable platform that provides distributed storage and computational capabilities on clusters of commodity hardware, and the second is the Mobile Distributed File System (MDFS) which allows distributed data storage with built-in reliability and security. By making the MDFS file system work with Hadoop on mobile devices, we hope to enable big data applications on tactical clouds.
Published in: 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet)The article of record as published may be found at http://dx.doi.org/10.1109/CloudNet.2014.6969015
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