  Efficient task partitioning plays a crucial role in achieving high
performance at multiprocessor plat forms. 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. The achieved results
demonstrate that the proposed partitioning scheme significantly surpasses
previous approaches in terms of both number of iterations and energy savings.

Operating Systems