Evaluating Task Execution Performance Under Energy Measurement Overhead

TL;DR

This paper investigates how energy measurement overhead affects task execution performance in energy harvesting IoT devices, identifying optimal measurement frequencies to maximize task completion rates.

Contribution

It introduces a comparison between energy-blind and energy-aware task scheduling approaches, highlighting the importance of tuning operational parameters for optimal performance.

Findings

Optimal energy measurement and task execution frequencies exist for maximizing task completion.

Incorrect parameter choices can cause energy-aware scheduling to underperform.

Energy measurement costs can negate the benefits of energy-aware scheduling.

Abstract

Energy-awareness for adapting task execution behavior can bring several benefits in terms of performance improvement in energy harvesting (EH) Internet of Things (IoT) devices. However, the energy measurement cost of acquiring energy information, which is traditionally ignored, can potentially neutralize or even reverse the potential benefits. This paper highlights operational parameters, such as energy measurement frequency and task execution frequency, which can be tuned to improve the task execution performance of an EH-IoT device. To this end, we consider energy-blind (EB) and energy-aware (EA) task decision approaches and compare their task completion rate performance. We show that, for specific hardware design parameters of an EH-IoT device, there exists an optimal energy measurement/task execution frequency that can maximize the task completion rate in both approaches. Moreover, if these parameters are not chosen appropriately, then energy measurement costs can cause EA scheduling to underperform compared to EB scheduling.