Quota Marketplace: Dynamic Pricing for Efficient Allocation of ML Training Resources
The escalating demand for Machine Learning (ML) training resources in recent years has resulted in a substantial gap between the high demand and the available supply. Efficient allocation of these scarce and expensive resources is crucial for organizations to maximize their return on investment. Existing resource allocation mechanisms, like Karma [OSDI'23], are designed to guarantee Pareto efficiency and max-min fairness in settings with dynamic (time-varying) user demands, but fail to preserve
Record details
Published: 9 July 2026
Source: arXiv fairness query
Category: Research
Topics: Bias & fairness · Finance, VC & PE
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (9 July 2026), “Quota Marketplace: Dynamic Pricing for Efficient Allocation of ML Training Resources,” evidence record 3052, https://ethics.ai/record/3052 (originally published by arXiv fairness query).
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