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The problem of determining whether a set of periodic tasks can be assigned to a set of heterogeneous processors in such a way that all timing constraints are met has been shown, in general, to be NP-hard. This paper presents a new algorithm based on Ant Colony Optimization (ACO) metaheuristic for solving this problem. Experimental results show that our ACO approach can outperform the major existing methods. In addition to being able to search for a feasible assignment solution, our ACO approach can further optimize the solution to reduce its energy consumption.

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Energy consumption in computer systems has become a more and more important issue. High energy consumption has already damaged the environment to some extent, especially in heterogeneous multiprocessors. In this paper, we first formulate and describe the energy-aware real-time task scheduling problem in heterogeneous multiprocessors. Then we propose a particle swarm optimization (PSO) based algorithm, which can successfully reduce the energy cost and the time for searching feasible solutions. Experimental results show that the PSO-based energy-aware metaheuristic uses 40%–50% less energy than the GA-based and SFLA-based algorithms and spends 10% less time than the SFLA-based algorithm in finding the solutions. Besides, it can also find 19% more feasible solutions than the SFLA-based algorithm.