Limits...
Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system.

Page AJ, Keane TM, Naughton TJ - J Parallel Distrib Comput (2010)

Bottom Line: It operates on batches of unmapped tasks and can preemptively remap tasks to processors.The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography.Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK.

ABSTRACT
We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms.

No MeSH data available.


The number of idle clients in the system while the set of problems is being processed with the authors’ scheduling algorithm (PN).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2927021&req=5

fig7: The number of idle clients in the system while the set of problems is being processed with the authors’ scheduling algorithm (PN).

Mentions: Fig. 7 shows the number of idle clients while the set of problems is being processed using the PN scheduler in a highly heterogeneous resource environment. The initial assignment of tasks to processors does not happen instantaneously because the client machines only contact the server at set intervals (1 min in this case). Near the end when the steep slope shows that all of the clients stop processing tasks within a short interval. If this was a shallow slope it would indicate processing resources are idle and underutilized.


Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system.

Page AJ, Keane TM, Naughton TJ - J Parallel Distrib Comput (2010)

The number of idle clients in the system while the set of problems is being processed with the authors’ scheduling algorithm (PN).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2927021&req=5

fig7: The number of idle clients in the system while the set of problems is being processed with the authors’ scheduling algorithm (PN).
Mentions: Fig. 7 shows the number of idle clients while the set of problems is being processed using the PN scheduler in a highly heterogeneous resource environment. The initial assignment of tasks to processors does not happen instantaneously because the client machines only contact the server at set intervals (1 min in this case). Near the end when the steep slope shows that all of the clients stop processing tasks within a short interval. If this was a shallow slope it would indicate processing resources are idle and underutilized.

Bottom Line: It operates on batches of unmapped tasks and can preemptively remap tasks to processors.The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography.Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK.

ABSTRACT
We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms.

No MeSH data available.