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Proceedings Paper

An adaptive process-based cloud infrastructure for space situational awareness applications
Author(s): Bingwei Liu; Yu Chen; Dan Shen; Genshe Chen; Khanh Pham; Erik Blasch; Bruce Rubin
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Paper Abstract

Space situational awareness (SSA) and defense space control capabilities are top priorities for groups that own or operate man-made spacecraft. Also, with the growing amount of space debris, there is an increase in demand for contextual understanding that necessitates the capability of collecting and processing a vast amount sensor data. Cloud computing, which features scalable and flexible storage and computing services, has been recognized as an ideal candidate that can meet the large data contextual challenges as needed by SSA. Cloud computing consists of physical service providers and middleware virtual machines together with infrastructure, platform, and software as service (IaaS, PaaS, SaaS) models. However, the typical Virtual Machine (VM) abstraction is on a per operating systems basis, which is at too low-level and limits the flexibility of a mission application architecture. In responding to this technical challenge, a novel adaptive process based cloud infrastructure for SSA applications is proposed in this paper. In addition, the details for the design rationale and a prototype is further examined. The SSA Cloud (SSAC) conceptual capability will potentially support space situation monitoring and tracking, object identification, and threat assessment. Lastly, the benefits of a more granular and flexible cloud computing resources allocation are illustrated for data processing and implementation considerations within a representative SSA system environment. We show that the container-based virtualization performs better than hypervisor-based virtualization technology in an SSA scenario.

Paper Details

Date Published: 3 June 2014
PDF: 9 pages
Proc. SPIE 9085, Sensors and Systems for Space Applications VII, 90850M (3 June 2014); doi: 10.1117/12.2053759
Show Author Affiliations
Bingwei Liu, Binghamton Univ. (United States)
Yu Chen, Binghamton Univ. (United States)
Dan Shen, Intelligent Fusion Technology, Inc. (United States)
Genshe Chen, Intelligent Fusion Technology, Inc. (United States)
Khanh Pham, Air Force Research Lab. (United States)
Erik Blasch, Air Force Research Lab. (United States)
Bruce Rubin, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 9085:
Sensors and Systems for Space Applications VII
Khanh D. Pham; Joseph L. Cox, Editor(s)

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