Paper Info
Title
Parallel extremal optimization in processor load balancing for distributed applications.
Abstract
Graphical abstractDisplay Omitted The paper concerns parallel methods for extremal optimization (EO) applied in processor load balancing in execution of distributed programs. In these methods EO algorithms detect an optimized strategy of tasks migration leading to reduction of program execution time. We use an improved EO algorithm with guided state changes (EO-GS) that provides parallel search for next solution state during solution improvement based on some knowledge of the problem. The search is based on two-step stochastic selection using two fitness functions which account for computation and communication assessment of migration targets. Based on the improved EO-GS approach we propose and evaluate several versions of the parallelization methods of EO algorithms in the context of processor load balancing. Some of them use the crossover operation known in genetic algorithms. The quality of the proposed algorithms is evaluated by experiments with simulated load balancing in execution of distributed programs represented as macro data flow graphs. Load balancing based on so parallelized improved EO provides better convergence of the algorithm, smaller number of task migrations to be done and reduced execution time of applications.
Year
DOI
Venue
2016
10.1016/j.asoc.2016.04.033
Appl. Soft Comput.
Keywords
Field
DocType
Distributed programs,Load balancing,Extremal optimization
Convergence (routing),Mathematical optimization,Crossover,Extremal optimization,Load balancing (computing),Computer science,Parallel computing,Macro,Genetic algorithm,Distributed computing,Data flow diagram,Computation
Journal
Volume
Issue
ISSN
46
C
1568-4946
Citations 
PageRank 
References 
2
0.39
27
Authors
6
Name
Order
Citations
PageRank
Ivanoe De Falco124234.58
Eryk Laskowski210718.85
Richard Olejnik3558.86
Umberto Scafuri411616.33
Ernesto Tarantino536142.45
Marek Tudruj627156.00