Real-Time Schedulability Analysis and Enhancement of Transiently Powered Processors With NVMs

Recent Internet-of-Things or Wireless Sensor Network devices are often operated with energy harvesters. As there are no energy storages in those devices, power is not consistently provided to the devices at all times. In such transiently powered systems, in order to keep the system reliable without losing any execution contexts, non-volatile memories (NVMs) are typically used for swift backup/restoration of execution contexts. In this article, we perform a real-time schedulability analysis of the transiently powered processors with NVMs. We first quantitatively characterize the charging and discharging behaviors of the energy harvester and extract the compute capability of the system in time interval domain. Then, based on Real-Time Calculus, we determine whether the given multi-task workload is schedulable or not with respect to the earliest deadline first (EDF) or fixed-priority (FP) scheduling policies. In addition, we study how the choice of the threshold voltage parameter affects the schedulability, then propose a feasible threshold selection algorithm to enhance schedulability. We verify the effectiveness of the proposed technique with extensive simulations. Compared to the naive selection method, the proposed technique always shows improvements in schedulability in various workloads.