Harvesting-Aware Power Management for Real-Time Systems With Renewable Energy

In this paper, we propose a harvesting-aware power management algorithm that targets at achieving good energy efficiency and system performance in energy harvesting real-time systems. The proposed algorithm utilizes static and adaptive scheduling techniques combined with dynamic voltage and frequency selection to achieve good system performance under timing and energy constraints. In our approach, we simplify the scheduling and optimization problem by separating constraints in timing and energy domains. The proposed algorithm achieves improved system performance by exploiting task slack with dynamic voltage and frequency selection and minimizing the waste on harvested energy. Experimental results show that the proposed algorithm improves the system performance in deadline miss rate and the minimum storage capacity requirement for zero deadline miss rate. Comparing to the existing algorithms, the proposed algorithm achieves better performance in terms of the deadline miss rate and the minimum storage capacity under various settings of workloads and harvested energy profiles.