# Treehouse: A Case For Carbon-Aware Datacenter Software

arXiv: https://arxiv.org/abs/2201.02120 · HTML: https://mlsanity.com/paper/2201.02120/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 72 references that are in the map.
- First posted on arXiv 6 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [Chasing Carbon: The Elusive Environmental Footprint of Computing](https://mlsanity.com/paper/2011.02839/) (2020)
- [Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider](https://mlsanity.com/paper/2003.03423/) (2020)
- [Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels](https://mlsanity.com/paper/1906.11879/) (2019)
- [Effectively Prefetching Remote Memory with Leap](https://mlsanity.com/paper/1911.09829/) (2019)
- [Data Centers as Dispatchable Loads to Harness Stranded Power](https://mlsanity.com/paper/1606.00350/) (2016)
- [Hydra: Resilient and Highly Available Remote Memory](https://mlsanity.com/paper/1910.09727/) (2019)
- [BPF for storage: an exokernel-inspired approach](https://mlsanity.com/paper/2102.12922/) (2021)

## Built on by (most cited first, 12 of 12)

- [Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training](https://mlsanity.com/paper/2208.06102/) (2022)
- [Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems](https://mlsanity.com/paper/2407.04014/) (2024)
- [Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads](https://mlsanity.com/paper/2407.00010/) (2024)
- [Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference](https://mlsanity.com/paper/2403.12900/) (2024)
- [Towards Environmentally Equitable AI via Geographical Load Balancing](https://mlsanity.com/paper/2307.05494/) (2023)
- [Green Federated Learning](https://mlsanity.com/paper/2303.14604/) (2023)
- [Beyond Efficiency: Scaling AI Sustainably](https://mlsanity.com/paper/2406.05303/) (2024)
- [PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices](https://mlsanity.com/paper/2310.19991/) (2023)
- [Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training](https://mlsanity.com/paper/2303.02508/) (2023)
- [A Robust Power Model Training Framework for Cloud Native Runtime Energy Metric Exporter](https://mlsanity.com/paper/2407.00878/) (2024)
- [LLM-Powered Predictive Decision-Making for Sustainable Data Center Operations](https://mlsanity.com/paper/2608.18503/) (2026)
- [Characterizing Job Power Elasticity for Power-Flexible AI Training](https://mlsanity.com/paper/2609.11542/) (2026)

## Abstract (arXiv)

The end of Dennard scaling and the slowing of Moore's Law has put the energy use of datacenters on an unsustainable path. Datacenters are already a significant fraction of worldwide electricity use, with application demand scaling at a rapid rate. We argue that substantial reductions in the carbon intensity of datacenter computing are possible with a software-centric approach: by making energy and carbon visible to application developers on a fine-grained basis, by modifying system APIs to make it possible to make informed trade offs between performance and carbon emissions, and by raising the level of application programming to allow for flexible use of more energy efficient means of compute and storage. We also lay out a research agenda for systems software to reduce the carbon footprint of datacenter computing.