Dynamic Task Scheduling Using Parallel Genetic Algorithms for Heterogeneous Distributed Computing
Authors -
Nedunchelian, Ramanujam;
Koushik, Kalyanaraman;
Meiyappan, Nagappan and
Raghu, Viswanathan
Venue -
In Proceedings of the International Conference on Grid Computing and Applications (GCA'06), Las Vegas, USA, Pages 82-88
Related Tags -
Abstract -
A parallel genetic algorithm has been developed
to dynamically schedule heterogeneous tasks to
heterogeneous processors in a distributed environment.
The scheduling problem is known to be NP complete.
Genetic algorithms, a meta-heuristic search technique, have been used successfully in this field. The proposed
algorithm uses multiple processors with centralized
control for scheduling. Tasks are taken as batches and are
scheduled to minimize the execution time and balance the
loads of the processors. According to our experimental
results, the proposed parallel genetic algorithm (PPGA)
considerably decreases the scheduling time without adversely affecting the maxspan of the resulting schedules.
Preprint -
PDF
BibTex -
@article{Ramanujam2006,
author = {Nedunchelian, Ramanujam and Koushik, Kalyanaraman and Meiyappan, Nagappan and Raghu, Viswanathan},
keyword = {Scheduling},
title = {Dynamic Task Scheduling Using Parallel Genetic Algorithms for Heterogeneous Distributed Computing},
type = {conference},
venue = {In Proceedings of the International Conference on Grid Computing and Applications (GCA'06), Las Vegas, USA, Pages 82-88}
}