Dynamic Tasks Scheduling Model for Performance Evaluation of a Distributed Computing System through Artificial Neural Network

As technology has quickly and relentlessly advanced in the field of computer hardware, Distributed Computing System [DCS] has become increasingly popular. Performance enhancement is one of the most important issues in distributed systems. In this paper we have proposed a dynamic task allocation model based on artificial neural network [ANN] scheduling approach to arrange the tasks to the processors. Relocating the tasks from one processor to another at certain points during the course of execution of the program that contributes to the total cost of the running program has been taken into account. Phase-wise Execution Cost [EC], Inter Task Communication Cost [ITCC], Residence Cost [RC] of each task on different processors and Relocation Cost [REC] for each task has been considered while preparing the model.