Multi-objective method for divisible load scheduling in multi-level tree network

There is extensive literature concerning the divisible load theory. Based on the divisible load theory (DLT) the load can be divided into some arbitrary independent parts, in which each part can be processed independently by a processor. The divisible load theory has also been examined on the processors that cheat the algorithm, i.e., the processors do not report their true computation rates. According to the literature, if the processors do not report their true computation rates, the divisible load scheduling model fails to achieve its optimal performance. This paper focuses on the divisible load scheduling, where the processors cheat the algorithm. In this paper, a multi-objective method for divisible load scheduling is proposed. The goal is to improve the performance of the divisible load scheduling when the processors cheat the algorithm. The proposed method has been examined on several function approximation problems. The experimental results indicate the proposed method has approximately 66% decrease in finish time in the best case

Divisible load scheduling of image processing applications on the heterogeneous star and tree networks using a new genetic algorithm

The divisible load scheduling of image processing applications on the heterogeneous star and multi-level tree networks is addressed in this paper. In our platforms, processors and network links have different speeds. In addition, 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. The closed-form solution for the processing time, the image fractions that should be allocated to each processor, the optimum number of participating processors, and the optimal sequence for load distribution are derived. The new concept of equivalent processor in tree network is introduced and 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