Year: 2025
Type: conference-paper
Abstract: Energy-harvesting sensors utilize local, ambient energy resources to operate and thus eliminate the need for batteries. A key challenge for such systems is avoiding power failures during application execution. Energy-aware runtimes avoid such failures by reasoning about the task's energy consumption and the current energy available to the system. However, the energy consumption estimates profiled by prior work fail to account for incoming energy, producing incorrect energy consumption estimates which could lead to power failures and missed deadlines. This work analyzes the impact of incoming energy on the profiled energy consumption and argues that future energy-aware runtimes must be mindful of harvested energy when profiling a task's energy consumption.
Institution: University of Chicago
Language: English
Topic: Real-Time Systems Scheduling
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: CNS Core: Medium: Accurate Anytime Learning for Energy andTimeliness in Software Systems, G1933111799, CSR: Medium: Improving the Interface between Machine Learning and Software Systems, SPX: Collaborative Research: Rethinking Data Center Abstractions Utilizing Warehouse-Scale Shared Memory, Collaborative Research: PPoSS: LARGE: ScaleStuds: Foundations for Correctness Checkability and Performance Predictability of Systems at Scale
OpenAlex ID: W4411150250