The carbon footprint of distributed cloud storage
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Abstract (arXiv)
The ICT (Information Communication Technologies) ecosystem is estimated to be responsible, as of today, for 10% of the total worldwide energy demand - equivalent to the combined energy production of Germany and Japan. Cloud storage, mainly operated through large and densely-packed data centers, constitutes a non-negligible part of it. However, since the cloud is a fast-inflating market and the energy-efficiency of data centers is mostly an insensitive issue for the collectivity, its carbon footprint shows no signs of slowing down. In this paper, we analyze a novel paradigm for cloud storage (implemented by Cubbit, http://cubbit.io), in which data are stored and distributed over a network of p2p-interacting ARM-based single-board devices. We compare Cubbit's distributed cloud to the traditional centralized solution in terms of environmental footprint and energy efficiency. We demonstrate that, compared to the centralized cloud, the distributed architecture of Cubbit has a carbon footprint reduced of a 77% factor for data storage and of a 50% factor for data transfers. These results provide an example of how a radical paradigm shift in a large-reach technology can benefit both the final consumer as well as our society as a whole.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Mar 2018.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2023LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Modelscited by 72
- 2023An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Codecited by 1
- 2024Power-Efficient Image Storage: Leveraging Super Resolution Generative Adversarial Network for Sustainable Compression and Reduced Carbon Footprintcited by 0
- 2026A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Modelcited by 0
- 2026A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model22 Sep 2026
- 2024Power-Efficient Image Storage: Leveraging Super Resolution Generative Adversarial Network for Sustainable Compression and Reduced Carbon Footprint6 Apr 2024
- 2023LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models25 Sep 2023
- 2023An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code5 Jul 2023