Divisible Load Scheduling of Image Processing Applications on the Heterogeneous Star Network Using a new Genetic Algorithm

Abstract

The divisible load scheduling of image processing applications on the heterogeneous star network is addressed in this paper. In our platform, processors and links have different speeds. Also the computation and communication overheads are considered. A new genetic algorithm for minimizing the processing time of low level image applications using divisible load theory is introduced. A closed form solution for the processing time and the image fractions that should be assigned to each processor are obtained. The optimum number of participating processors and the optimal sequence for load distribution with a new genetic algorithm are derived. The effect of different image and kernel sizes on processing time and speed up are investigated. Finally, to indicate the efficiency of our algorithm, several numerical experiments are presented.