Multiprocessor scheduling using mean-field annealing

This paper presents our work on the static task scheduling model using the mean-field annealing (MFA) technique. Meanfield 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 has the advantage of faster convergence to the equilibrium temperature, compared to simulated annealing. Our experimental work verifies this finding, besides making comparison on the effectiveness of the model on various network and task graph sizes. Our work also includes the simulation of the MFA model on several network topologies using varying parameters. The MFA simulation model is targeted on nonpreemptive and precedence-related tasks with communication costs.