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Published Articles >> Table of Contents >> Abstract
18th International Parallel and Distributed Processing Symposium (IPDPS'04) - Workshop 2
p. 116a
Resource Management of Highly Configurable Tasks
Jeffery P. Hansen, Carnegie Mellon University
Sourav Ghosh, Carnegie Mellon University
Ragunathan Rajkumar, Carnegie Mellon University
John Lehoczky, Carnegie Mellon University
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IPDPS.2004.1303070
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| Abstract |
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In this paper we present an extension to our QoS optimization algorithm, Q-RAM[7] [11], that can improve optimization time by several orders of magnitude when managing highly configurable tasks. A highly configurable task is one with a large number of QoS dimensions and/or a large number of quality levels on those dimensions. For example, an application that has ten QoS dimensions with ten quality levels each will have 10^10 setpoints, or ways in which it can be configured. While the existing Q-RAM algorithm has been shown to be a very effective resource management tool, it must still explicitly perform computations on all of the setpoints for each task. For tasks with 10^10 setpoints or more, this is clearly impractical. The key idea presented here is a new approximation algorithm for the concave majorant step in Q-RAM. By using this algorithm in a filtering step, the best performing subset of the setpoints can be quickly found without explicitly examining all of the setpoints. The idea is validated using a phased array radar system as an example application.
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 Additional Information
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Citation:
Jeffery P. Hansen, Sourav Ghosh, Ragunathan Rajkumar, John Lehoczky,
"Resource Management of Highly Configurable Tasks,"
ipdps,
p. 116a,
18th International Parallel and Distributed Processing Symposium (IPDPS'04) - Workshop 2,
2004
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