Energy-aware Scheduling and Input Buffer Overflow Prevention for Energy-harvesting Systems
Year: 2025
Type: conference-paper
Abstract: Modern energy-harvesting devices use on-device compute to discard data uninteresting to the application, improving energy availability. These devices capture data at fixed rate, and process captured data at a rate that varies with environmental factors like input power and event activity. If capture rate exceeds processing rate, new inputs are stored in a small on-device input buffer (hundreds of kBs). When the input buffer fills up, the device discards newer inputs, missing potentially interesting events. Energy-harvesting devices must avoid such input buffer overflows (IBO) to avoid missing interesting events. A static solution to IBOs is impossible given dynamic variations in processing rate, and prior research fails to provide a suitable dynamic solution. We propose Quetzal, a new hardware-software solution targeted at avoiding IBOs. Quetzal's software has two parts: a new energy-aware scheduler that selects jobs with the lowest end-to-end latency (including energy recharging), and a runtime which uses queueing-theory to predict if the selected job will cause IBOs. Quetzal reacts to imminent IBOs by degrading the scheduled job. Quetzal's scheduler and runtime use a simple, system-agnostic hardware circuit to measure power at runtime. Quetzal reduces events missed due to IBOs by up to 4.2× compared to several baselines.
Authors: Harsh Desai, Xinye Wang, Brandon Lucia
Institution: Carnegie Mellon University
Language: English
Topic: Real-Time Systems Scheduling
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408903278