Autonomic Performance Assurance for Multi-Processor Supervisory Control

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-12-C-0153
Agency Tracking Number: O11B-T01-1021
Amount: $99,993.00
Phase: Phase I
Program: STTR
Awards Year: 2012
Solicitation Year: 2011
Solicitation Topic Code: OSD11-T01
Solicitation Number: 2011.B
Small Business Information
Colorado Engineering Inc.
1310 United Heights Suite 105, Colorado Springs, CO, -
DUNS: 131860632
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Michael Hammel
 Principal Engineer
 (719) 388-8582
 michael.hammel@coloradoengineeringinc.com
Business Contact
 Nancy Scally
Title: CEO
Phone: (719) 388-8582
Email: nancy.scally@coloradoengineeringinc.com
Research Institution
 University of Colorado (UCCS)
 Joe Zhou
 1420 Austin Bluffs Parkway
Colorado Springs, CO, 80918-
 (719) 255-3493
 Nonprofit college or university
Abstract
Multi-processor computing systems are growing in capacity and usage. They encompass multiple, distributed implementations as well as heterogeneous, embedded computing architectures. The processing density enabled by such approaches holds promise for unmanned combat air vehicles (UCAVs) with their plethora of mission sensors and command and control processing requirements. However, the software and middleware required to effectively (and efficiently) harness multi-processor computing power is lacking. The team of Colorado Engineering, Inc. (CEI) and the University of Colorado, Colorado Springs (UCCS) proposes to develop an autonomic performance assurance framework and associated techniques that support automated job scheduling and guarantee performance for multi-processor supervisory control. Through the development, verification, and validation of the proposed framework, the project will increase the autonomy and capability of unmanned combat air vehicles (UCAV). Autonomic performance control is crucial to computer controls in military operations with highly dynamic constraints and topologies. However, it is significantly challenging because the performance is the result of a complex interaction of complex workloads in a complex underlying computer system. UCCS will draw on its research into autonomic computing while CEI will leverage its experience with distributed/grid computing and heterogeneous, high performance, multi-processor embedded computing architectures for UCAVs to guide the proposed activities.

* information listed above is at the time of submission.

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