Junkyard Computing: Repurposing Discarded Smartphones to Minimize Carbon
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Abstract (arXiv)
1.5 billion smartphones are sold annually, and most are decommissioned less than two years later. Most of these unwanted smartphones are neither discarded nor recycled but languish in junk drawers and storage units. This computational stockpile represents a substantial wasted potential: modern smartphones have increasingly high-performance and energy-efficient processors, extensive networking capabilities, and a reliable built-in power supply. This project studies the ability to reuse smartphones as "junkyard computers." Junkyard computers grow global computing capacity by extending device lifetimes, which supplants the manufacture of new devices. We show that the capabilities of even decade-old smartphones are within those demanded by modern cloud microservices and discuss how to combine phones to perform increasingly complex tasks. We describe how current operation-focused metrics do not capture the actual carbon costs of compute. We propose Computational Carbon Intensity -- a performance metric that balances the continued service of older devices with the superlinear runtime improvements of newer machines. We use this metric to redefine device service lifetime in terms of carbon efficiency. We develop a cloudlet of reused Pixel 3A phones. We analyze the carbon benefits of deploying large, end-to-end microservice-based applications on these smartphones. Finally, we describe system architectures and associated challenges to scale to cloudlets with hundreds and thousands of smartphones.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Oct 2021.
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Citing papers per year: 2023: 2; 2024: 2; 2025: 1; 2026: 1.
6 papers.
- 2023Towards Environmentally Equitable AI via Geographical Load Balancingcited by 10
- 2023CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centerscited by 6
- 2024Harnessing Your DRAM and SSD for Sustainable and Accessible LLM Inference with Mixed-Precision and Multi-level Cachingcited by 3
- 2024LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demandcited by 2
- 2025Towards Decentralized and Sustainable Foundation Model Training with the Edgecited by 2
- 2026Memory as a Wasting Asset: Pricing Flash Endurance for Embodied Agents, and the Limits of Doing Socited by 0
- 2026Memory as a Wasting Asset: Pricing Flash Endurance for Embodied Agents, and the Limits of Doing So16 Jun 2026
- 2025Towards Decentralized and Sustainable Foundation Model Training with the Edge2 Jul 2025
- 2024Harnessing Your DRAM and SSD for Sustainable and Accessible LLM Inference with Mixed-Precision and Multi-level Caching17 Oct 2024
- 2024LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand29 Mar 2024
- 2023CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers6 Nov 2023
- 2023Towards Environmentally Equitable AI via Geographical Load Balancing20 Jun 2023