arXiv:2606.10472v1 Announce Type: cross Abstract: Dynamic multi-resource allocation is a central problem in shared computing environments, where users' demands arrive sequentially and resources must be distributed fairly without knowledge of future demands. Existing methods emphasize fairness guarantees such as Sharing Incentive, Envy Freeness, and Dynamic Pareto Optimality, but often overlook system utility. Moreover, these fairness criteria are mutually incompatible, preventing strict enforcement of them at the same time. We propose a neural allocation mechanism that reconciles fairness with
The increasing complexity and scale of AI systems require more sophisticated resource allocation mechanisms, particularly in environments with sequential and unpredictable demand.
Optimizing resource division while balancing fairness and utility is critical for the efficient operation of large-scale AI and multi-user computing infrastructures.
This research proposes a method to reconcile previously incompatible goals of fairness and utility in dynamic resource allocation, potentially leading to more robust and equitable AI systems.
- · Cloud computing providers
- · AI model developers
- · Data center operators
- · Users of shared AI resources
- · Inefficient resource allocation algorithms
- · Systems with strict, inflexible fairness criteria
Improved efficiency and fairness in shared computing and AI resource environments.
Faster development and deployment of larger, more complex AI models due to better resource utilization.
Reduced operational costs for AI infrastructure and potentially broader access to advanced computing resources.
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Read at arXiv cs.LG