Application energy efficiency can be improved by executing each application component on the compute element that consumes the least energy while also satisfying time constraints. In principle, the function as a service (FaaS) paradigm should simplify such optimizations by abstracting away compute location, but existing FaaS systems do not provide for user transparency over application energy consumption or task placement. Here we present GreenFaaS, a novel open source framework that bridges this gap between energy-efficient applications and FaaS platforms. GreenFaaS can be deployed by end users or providers across systems to monitor energy use, provide task-specific feedback, and schedule tasks in an energy-aware manner. We demonstrate that intelligent placement of tasks can both reduce energy consumption and improve performance. For a synthetic workload, GreenFaaS reduces the energy-delay product by 45% compared to alternatives. Furthermore, running a molecular design application through GreenFaaS can reduce energy consumption by 21% and runtime by 63% by better matching tasks with machines.
@article{arxiv.2406.17710,
title = {GreenFaaS: Maximizing Energy Efficiency of HPC Workloads with FaaS},
author = {Alok Kamatar and Valerie Hayot-Sasson and Yadu Babuji and Andre Bauer and Gourav Rattihalli and Ninad Hogade and Dejan Milojicic and Kyle Chard and Ian Foster},
journal= {arXiv preprint arXiv:2406.17710},
year = {2024}
}