Hopfield Neural Network Approach for Task Scheduling in a Grid Environment

A Hopfield neural network-based approach for task scheduling in a Grid environment is proposed in this paper. All constraints and the optimization object of task scheduling problem in Grid are developed and included in the computational energy function of neural network. To avoid Hopfield neural network converge into local minimum volume, the simulated annealing algorithms are applied to the network. Thus the global minimum of the network as a feasible solution for Grid task scheduling is achieved. The theoretic analyses and simulation experiments have manifested the approach's effectiveness.