A genetic algorithm for multiprocessor scheduling

Abstract

The problem of multiprocessor scheduling can be stated as finding a schedule for ageneral task graph to be executed on a multiprocessor system so that the schedulelength can be minimized. This scheduling problem is known to be NP-hard, and methodsbased on heuristic search have been proposed to obtain optimal and suboptimal solutions.Genetic algorithms have recently received much attention as a class of robust stochasticsearch algorithms for various optimization problems. In this paper, an efficient methodbased on genetic algorithms is developed to solve the multiprocessor scheduling problem.The representation of the search node is based on the order of the tasks being executedin each individual processor. The genetic operator proposed is based on the precedencerelations between the tasks in the task graph. Simulation results comparing the proposedgenetic algorithm, the list scheduling algorithm, and the optimal schedule using randomtask graphs, and a robot inverse dynamics computational task graph are presented.