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Eighth International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2007)
Task Allocation in Distributed Embedded Systems by Genetic Programming
Adelaide, Australia
December 03-December 06
ISBN: 0-7695-3049-4
ASCII Text
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Allan Tengg, Andreas Klausner, Bernhard Rinner, "Task Allocation in Distributed Embedded Systems by Genetic Programming," Parallel and Distributed Computing Applications and Technologies, International Conference on, pp. 26-30, Eighth International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2007), 2007.
 
 
BibTex
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@article{ 10.1109/PDCAT.2007.41,
author = {Allan Tengg and Andreas Klausner and Bernhard Rinner},
title = {Task Allocation in Distributed Embedded Systems by Genetic Programming},
journal ={Parallel and Distributed Computing Applications and Technologies, International Conference on},
volume = {0},
year = {2007},
isbn = {0-7695-3049-4},
pages = {26-30},
doi = {http://doi.ieeecomputersociety.org/10.1109/PDCAT.2007.41},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Parallel and Distributed Computing Applications and Technologies, International Conference on
TI - Task Allocation in Distributed Embedded Systems by Genetic Programming
SN - 0-7695-3049-4
SP26
EP30
A1 - Allan Tengg,
A1 - Andreas Klausner,
A1 - Bernhard Rinner,
PY - 2007
VL - 0
JA - Parallel and Distributed Computing Applications and Technologies, International Conference on
ER -
 
In this paper we describe a task allocation method, that utilizes genetic programming to find a suitable solution in an adequate time for this NP-complete combinatorial op- timization problem. The underlying distributed embedded system is heterogenous, consisting of different processors with different properties such as core type, clock frequency, available memory, and I/O interfaces, interconnected with different communication media. In our applications, which are described as data flow graphs, the number of tasks to be placed is much larger than the number of processors avail- able. We highlight the difficulties when applying genetic programming to this problem and present our solutions and enhancements, accompanied with some simulation results.
Citation:
Allan Tengg, Andreas Klausner, Bernhard Rinner, "Task Allocation in Distributed Embedded Systems by Genetic Programming," pdcat, pp.26-30, Eighth International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2007), 2007
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