Communication-Aware Task Scheduling for Energy-Harvesting Nonvolatile Processors

Abstract

With the advent of the Internet of Things (IoT), energy-harvesting nonvolatile processors (NVPs) have become promising platforms due to their durability when running on an intermittent power supply and fast read/write operations. However, the penalties caused by an increasing amount of data to be processed and growing communication demands pose critical challenges in the scheduling of tasks on energy-harvesting NVP platforms with tight energy and latency budgets. Together with the high power switching overhead that is induced under unstable power conditions, an increase in the amount of data to be processed significantly degrades the system performance. To overcome the problems of high communication and switching overheads for energy-harvesting NVP platforms, this article proposes a novel communication-aware task-scheduling technique. The algorithm first selects one or more executable tasks to be performed based on the task benefits and then calls a task partitioning algorithm to dynamically divide the scheduled tasks. We evaluate the performance of our proposed algorithm in comparison with the performance-aware task scheduling (PATH) and greedy iterative (GI) algorithms. Experimental results show that the proposed algorithm can reduce the execution time by 17.08% and 13.72% on average compared with the PATH and GI algorithms, respectively.