Citation
Heuristics for Divisible Loads Scheduling in Systems with Limited Memory. Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2009), 10-12 Aug 2009, Dublin, Ireland, pages 321-329, 2009.
Paper
Abstract
In this paper we study divisible load scheduling in systems with limited memory. Divisible loads represent computations which can be arbitrarily divided into parts and performed independently in parallel. The scheduling problem consists in distributing the load in a heterogeneous system taking into account communication and computation times, and limited memory buffers, so that the whole processing time is as short as possible. The amount of memory available at the processors is too small to accommodate the whole load at once. Thus the load is distributed in many small installments. Since our scheduling problem is computationally hard, we propose a genetic algorithm and several heuristics. We analyze their performance in a series of computational experiments. By comparing quality and cost of the solutions we demonstrate incapability of certain classes of heuristics.
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Bibtex
@INPROCEEDINGS{2009-321-329-P, author = {J. Berlinska and M. Drozdowski},
title = {Heuristics for Divisible Loads Scheduling in Systems with Limited Memory},
booktitle = {Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2009), 10-12 Aug 2009, Dublin, Ireland},
year = {2009},
editor = {J. Blazewicz and M. Drozdowski and G. Kendall and B. McCollum},
pages = {321--329},
note = {Paper},
abstract = {In this paper we study divisible load scheduling in systems with limited memory. Divisible loads represent computations which can be arbitrarily divided into parts and performed independently in parallel. The scheduling problem consists in distributing the load in a heterogeneous system taking into account communication and computation times, and limited memory buffers, so that the whole processing time is as short as possible. The amount of memory available at the processors is too small to accommodate the whole load at once. Thus the load is distributed in many small installments. Since our scheduling problem is computationally hard, we propose a genetic algorithm and several heuristics. We analyze their performance in a series of computational experiments. By comparing quality and cost of the solutions we demonstrate incapability of certain classes of heuristics.},
owner = {gxk},
timestamp = {2010.10.11},
webpdf = {2009-321-329-P.pdf} }