Energy-neutral Internet of Things requires freeing embedded devices from batteries and powering them from ambient energy. Ambient energy is, however, unpredictable and can only power a device intermittently. Therefore, the paradigm of intermittent execution is to save the program state into non-volatile memory frequently to preserve the execution progress. In task-based intermittent programming, the state is saved at task transition. Tasks are fixed at compile time and agnostic to energy conditions. Thus, the state may be saved either more often than necessary or not often enough for the program to progress and terminate. To address these challenges, we propose Coala, an adaptive and efficient task-based execution model. Coala progresses on a multi-task scale when energy permits and preserves the computation progress on a sub-task scale if necessary. Coala’s specialized memory virtualization mechanism ensures that power failures do not leave the program state in non-volatile memory inconsistent. Our evaluation on a real energy-harvesting platform not only shows that Coala reduces runtime by up to 54% as compared to a state-of-the-art system, but also it is able to progress where static systems fail.

中文翻译：

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能量收集设备的基于动态任务的间歇执行

能源中性物联网需要将嵌入式设备从电池中解放出来，并从环境能源中为其供电。然而，环境能量是不可预测的，只能间歇性地为设备供电。因此，间歇执行的范式是经常将程序状态保存到非易失性存储器中，以保存执行进度。在基于任务的间歇编程中，状态在任务转换时保存。任务在编译时是固定的，并且与能量条件无关。因此，状态可能被保存得比必要的更多，或者被保存的频率不足以让程序继续进行和终止。为了应对这些挑战，我们提出了 Coala，一种自适应且高效的基于任务的执行模型。Coala 在能量允许的情况下以多任务规模进行，并在必要时在子任务规模上保留计算进度。Coala 专门的内存虚拟化机制确保电源故障不会使非易失性内存中的程序状态不一致。我们对真实能量收集平台的评估不仅表明，与最先进的系统相比，Coala 将运行时间减少了高达 54%，而且它还能够在静态系统出现故障的情况下取得进展。