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Adaptive Resource Management for Uncertain Execution Platforms

Lindberg, Mikael LU (2010) In Research Reports TFRT-3249
Abstract
Embedded systems are becoming increasingly complex. At the same time, the components that make up the system grow more uncertain in their properties. For example, current developments in CPU design focuses on optimizing for average performance rather than better worst case performance. This, combined with presence of 3rd party software components with unknown properties, makes resource management using prior knowledge less and less feasible.



This thesis presents results on how to model software components so that resource allocation decisions can be made on-line. Both the single and multiple resource case is considered as well as extending the models to include resource constraints based on hardware dynam- ics.... (More)
Embedded systems are becoming increasingly complex. At the same time, the components that make up the system grow more uncertain in their properties. For example, current developments in CPU design focuses on optimizing for average performance rather than better worst case performance. This, combined with presence of 3rd party software components with unknown properties, makes resource management using prior knowledge less and less feasible.



This thesis presents results on how to model software components so that resource allocation decisions can be made on-line. Both the single and multiple resource case is considered as well as extending the models to include resource constraints based on hardware dynam- ics. Techniques for estimating component parameters on-line are presented. Also presented is an algorithm for computing an optimal allocation based on a set of convex utility functions. The algorithm is designed to be computationally efficient and to use simple mathematical expres- sions that are suitable for fixed point arithmetics. An implementation of the algorithm and results from experiments is presented, showing that an adaptive strategy using both estimation and optimization can outperform a static approach in cases where uncertainty is high. (Less)
Please use this url to cite or link to this publication:
author
supervisor
organization
publishing date
type
Thesis
publication status
published
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in
Research Reports TFRT-3249
publisher
Department of Automatic Control, Lund Institute of Technology, Lund University
ISSN
0280-5316
language
English
LU publication?
yes
id
4d4d80ec-b633-460a-a1ff-3c67ad4c6908 (old id 1748358)
date added to LUP
2016-04-04 11:58:33
date last changed
2018-11-21 21:08:18
@misc{4d4d80ec-b633-460a-a1ff-3c67ad4c6908,
  abstract     = {{Embedded systems are becoming increasingly complex. At the same time, the components that make up the system grow more uncertain in their properties. For example, current developments in CPU design focuses on optimizing for average performance rather than better worst case performance. This, combined with presence of 3rd party software components with unknown properties, makes resource management using prior knowledge less and less feasible.<br/><br>
<br/><br>
This thesis presents results on how to model software components so that resource allocation decisions can be made on-line. Both the single and multiple resource case is considered as well as extending the models to include resource constraints based on hardware dynam- ics. Techniques for estimating component parameters on-line are presented. Also presented is an algorithm for computing an optimal allocation based on a set of convex utility functions. The algorithm is designed to be computationally efficient and to use simple mathematical expres- sions that are suitable for fixed point arithmetics. An implementation of the algorithm and results from experiments is presented, showing that an adaptive strategy using both estimation and optimization can outperform a static approach in cases where uncertainty is high.}},
  author       = {{Lindberg, Mikael}},
  issn         = {{0280-5316}},
  language     = {{eng}},
  note         = {{Licentiate Thesis}},
  publisher    = {{Department of Automatic Control, Lund Institute of Technology, Lund University}},
  series       = {{Research Reports TFRT-3249}},
  title        = {{Adaptive Resource Management for Uncertain Execution Platforms}},
  url          = {{https://lup.lub.lu.se/search/files/5898404/8168917.pdf}},
  year         = {{2010}},
}