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

Load Scheduling for Bioinformatics Applications in Large Scale Networks

A load scheduling strategy with near-optimal processing time is designed to explore the computational characteristics of DNA sequence alignment algorithms, specifically, the Needleman-Wunsch Algorithm. Following the divisible load scheduling theory, we design an efficient load scheduling strategy to manage such bioinformatics applications in a large-scale network so that the overall processing time of the sequencing tasks is minimized. The row-wise and column-wise partitioning of the workload is adopted in the scheduling strategy. In this study, the load distribution depends on the length of the sequence and number of processors in the network and, the total processing time is also affected by communication link speed. We considered several cases in our study by varying the sequences, communication and computation speeds, and number of processors. Through simulation and numerical analysis, this study demonstrates that for a constant sequence length as the numbers of processors increase in the network the processing time for the job decreases and minimum overall processing time is achieved.Computer Science Departmen

Scheduling multiple divisible loads on a linear processor network

Min, Veeravalli, and Barlas have recently proposed strategies to minimize the overall execution time of one or several divisible loads on a heterogeneous linear network, using one or more installments. We show on a very simple example that their approach does not always produce a solution and that, when it does, the solution is often suboptimal. We also show how to find an optimal schedule for any instance, once the number of installments per load is given. Then, we formally state that any optimal schedule has an infinite number of installments under a linear cost model as the one assumed in the original papers. Therefore, such a cost model cannot be used to design practical multi-installment strategies. Finally, through extensive simulations we confirmed that the best solution is always produced by the linear programming approach, while solutions of the original papers can be far away from the optimal

The effect of start-up delays in scheduling divisible loads on bus networks: An alternate approach

AbstractIn this paper, scheduling of divisible loads in a bus network is considered. The objective is to minimize the processing time by including the overhead component due to start-up time that could degrade the performance of the system, in addition to the inherent communication and computation delays. These overheads are considered to be constant additive factors to the communication and computation components. A closed-form expression for optimal processing time is derived. Using this closed-form expression, this paper analytically proves significant results regarding the optimal sequence of load distribution and optimal number of processors. Numerical examples are presented to illustrate the analysis