Leakage-aware energy-efficient partitioning for real-time tasks on multiprocessor systems

Many embedded real-time systems require to process at high throughput while meeting real-time constraints. To improve performance of systems, multiprocessors have been used widely. Reduction of energy consumption is one of the most important issues in such systems, which operate with limited system resources. From the point of view of real-time scheduling, many approaches which save energy consumption have been proposed. Most of previous works adopt energy models without leakage energy because they assume that switching energy dominates. However, with the CMOS technology scaling, leakage energy has become a significant factor in overall energy consumption and previous works which only target reduction of switching energy are not always effective. In this paper, we propose two leakage-aware energy-efficient partitioning techniques named Suboptimal and Leakage-Aware Load Balancing (LALB) in multiprocessors. Suboptimal firstly determines the number of processors to minimize energy consumption and then assigns tasks uniformly. On the other hand, LALB firstly assigns tasks into all processors uniformly and then decreases the number of processors. We discuss time complexity of the proposed techniques and its feasibility. Simulation results show that the proposed techniques reduce energy consumption by an average of about 22% compared to existing techniques when leakage energy is dominant.