Energy-Aware Task Partitioning on Heterogeneous Multiprocessor Platforms
Efficient task partitioning plays a crucial role in achieving high performance at multiprocessor platforms. This paper addresses the problem of energy-aware static partitioning of periodic real-time tasks on heterogeneous multiprocessor platforms. A Particle Swarm Optimization variant based on Min-min technique for task partitioning is proposed. The proposed approach aims to minimize the overall energy consumption, meanwhile avoid deadline violations. An energy-aware cost function is proposed to be considered in the proposed approach. Extensive simulations and comparisons are conducted in order to validate the effectiveness of the proposed technique.