Multiprocessor scheduling using mean-field annealing

This paper presents our work on the static task scheduling model using the mean-field annealing (MFA) technique. Mean-field annealing is a technique of thermostatic annealing that takes the statistical properties of particles as its learning paradigm. It combines good features from the Hopfield neural network and simulated annealing, to overcome their weaknesses and improve on their performances. Our MFA model for task scheduling is derived from its prototype, namely, the graph partitioning problem. MFA is deterministic in nature and this gives the advantage of faster convergence to the equilibrium temperature, compared to simulated annealing. Our experimental work verifies this finding on various network and task graph sizes. Our work also includes the simulation of the MFA model on several network topologies and parameters.