Performance-aware task scheduling for energy harvesting nonvolatile processors considering power switching overhead

Nonvolatile processors have manifested strong vitality in battery-less energy harvesting sensor nodes due to their characteristics of zero standby power, resilience to power failures and fast read/write operations. However, I/O and sensing operations cannot store their system states after power off, hence they are sensitive to power failures and high power switching overhead is induced during power oscillation, which significantly degrades the system performance. In this paper, we propose a novel performance-aware task scheduling technique considering power switching overhead for energy harvesting nonvolatile processors. We first give the analysis of the power switching overhead on energy harvesting sensor nodes. Then, the scheduling problem is formulated by MILP (Mixed Integer Linear Programming). Furthermore, a task splitting strategy is adopted to improve the performance and an heuristic scheduling algorithm is proposed to reduce the problem complexity. Experimental results show that the proposed scheduling approach can improve the performance by 14% on average compared to the state-of-the-art scheduling strategy. With the employment of the task splitting approach, the execution time can be further reduced by 10.6%.