STOC: Secure, Tactical On-Demand Cloud

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$99,988.00
Award Year:
2011
Program:
STTR
Phase:
Phase I
Contract:
FA8750-11-C-0165
Agency Tracking Number:
F10B-T03-0208
Solicitation Year:
2010
Solicitation Topic Code:
AF10-BT03
Solicitation Number:
2010.B
Small Business Information
Harmonia, Inc.
2020 Kraft Drive, Suite 1000, Blacksburg, VA, -
Hubzone Owned:
Y
Minority Owned:
Y
Woman Owned:
Y
Duns:
556397615
Principal Investigator:
Marc Abrams
PI
(540) 951-5901
mabrams@harmonia.com
Business Contact:
Pallabi Saboo
CEO
(540) 951-5915
psaboo@harmonia.com
Research Institution:
Virginia Tech
Edwina Lamm
1880 Pratt Dr, Suite 2006
Blacksburg, VA, 24060-
(540) 231-5281
Nonprofit college or university
Abstract
ABSTRACT: We present a novel architecture for on-demand clouds, which means creating a cloud computing environment when needed that opportunistically takes advantage of available processors. The processors are located on mobile computers at the tactical edge of a network and connect via wireless networks. Clusters of processors can be interconnected by trunks. Compute nodes may appear or disappear unpredictably (e.g., nodes may be on disposable handhelds or moving Unmanned Aerial Systems, or may be damaged in a combat environment). Thus we examine survivable fault tolerant parallel computing frameworks, such as MapReduce, which can adapt resources on the fly when processors fail. We focus on exploiting Graphical Processing Units (GPUs), which offer a greater density of processing cores compared to CPUs given the same physical space limits, and are well suited to many numerical calculations (e.g., data fusion) involved with sensor data. We examine how to implement MapReduce for GPUs in a new way that takes advantage of emerging capabilities in GPU instruction sets to avoid multi-pass requirements of past work in this area. We devise algorithms that can seamlessly combine GPUs and CPUs by using the OpenCL language for coding. Our target problem is distributed 3D scene reconstruction on demand. BENEFIT: This work adapts clouds to a tactical edge environment, where commercial clouds (e.g., from Amazon, Google) are not designed to work. One benefit is to enable on-demand cloud computing with virtualization that offers secure processing on an untrusted infrastructure. This includes security controls providing confidentiality and integrity, verified through cryptographic proofs in accordance with NIST 800-53. Through the use of GPU chips, another benefit is enabling real-time response to decentralized tactical users by exploiting more massive parallelism for a given size, weight, and power limit than conventional Central Processing Units (CPUs) can achieve. GPU chips are approaching a thousand or more stream cores (e.g., 6 GFLOPS [giga floating point operations per second] per watt for one chip). We also allow cloud computing with seamless distribution of computation over heterogeneous GPU/CPU nodes, which is ideal for a tactical setting that may combine various types of hardware devices with and without GPUs. We also simplify the MapReduce framework for end users to allow users with lower expertise and programming skills to configure computations; this allows faster deployment of new capabilities on our cloud architecture.

* information listed above is at the time of submission.

Agency Micro-sites


SBA logo

Department of Agriculture logo

Department of Commerce logo

Department of Defense logo

Department of Education logo

Department of Energy logo

Department of Health and Human Services logo

Department of Homeland Security logo

Department of Transportation logo

Enviromental Protection Agency logo

National Aeronautics and Space Administration logo

National Science Foundation logo
US Flag An Official Website of the United States Government