mlsanityData to 7 Oct 2026

Treehouse: A Case For Carbon-Aware Datacenter Software

arXiv 2201.02120 ↗First posted 6 Jan 2022cs.DC cs.CY cs.LGcited by 12

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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.

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

Builds on

7 of 72 references.

References · 7

  1. 2020Chasing Carbon: The Elusive Environmental Footprint of Computingcited by 71
  2. 2020Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Providercited by 65
  3. 2019Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernelscited by 8
  4. 2019Effectively Prefetching Remote Memory with Leapcited by 4
  5. 2016Data Centers as Dispatchable Loads to Harness Stranded Powercited by 2
  6. 2019Hydra: Resilient and Highly Available Remote Memorycited by 1
  7. 2021BPF for storage: an exokernel-inspired approachcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 5; 2025: 0; 2026: 2.

12 papers.

  1. 2022Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Trainingcited by 78
  2. 2024Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systemscited by 27
  3. 2024Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloadscited by 17
  4. 2024Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inferencecited by 13
  5. 2023Towards Environmentally Equitable AI via Geographical Load Balancingcited by 10
  6. 2023Green Federated Learningcited by 8
  7. 2024Beyond Efficiency: Scaling AI Sustainablycited by 8
  8. 2023PolyThrottle: Energy-efficient Neural Network Inference on Edge Devicescited by 5
  9. 2023Chasing Low-Carbon Electricity for Practical and Sustainable DNN Trainingcited by 2
  10. 2024A Robust Power Model Training Framework for Cloud Native Runtime Energy Metric Exportercited by 1
  11. 2026LLM-Powered Predictive Decision-Making for Sustainable Data Center Operationscited by 0
  12. 2026Characterizing Job Power Elasticity for Power-Flexible AI Trainingcited by 0