An Efficient Tasks Scheduling Model in Distributed Processing Systems Using ANN

The single communication channel is shared by all the processors for Inter-Processor Communication (IPC) in a distributed system. A program whose execution is distributed among several processors in a distributed system has the total processing cost equal to the sum of Execution Costs (EC) and Inter-Processor Communication, which are function of the amount of data transmitted. An optimal assignment is a distribution of tasks that has lowest total execution cost. The neural network architecture is used to form tasks clusters between maximally linked tasks and to restrict the clusters size, the average load to be assigned to each processor. The Artificial Neural Network (ANN) based model has been discussed in this paper. The model provides near to optimal solution for assigning a set of “m” tasks of a program to set of “n” processors where “m >> n” in such a way that allocated load on all processors is balanced according to the average load. For testing the efficiency of the algorithm, several sets of input data have been considered for all program Pradeep Kumar Yadav, M.P. Singh, Avanish Kumar and Babita Agarwal