Trading Utility for Dynamic Fairness in Multiple Resource Division with Sequential Demand

2026-06-09

AI Summary

This work addresses the challenge of resource allocation in shared computing environments where user demands arrive sequentially and are unknown in advance, making it difficult for existing methods to simultaneously achieve high system utility and satisfy multiple mutually exclusive dynamic fairness criteria—such as sharing incentives, envy-freeness, and dynamic Pareto optimality. The authors propose a neural network–based end-to-end allocation mechanism that, for the first time, formalizes diverse fairness notions into differentiable, stepwise loss functions. Their approach integrates non-wastefulness constraints with demand subspace projection and introduces an elastic overcommitment strategy to exploit redundant resources. Experimental results demonstrate that the proposed method significantly improves system utility while maintaining strong fairness guarantees, thereby revealing a Pareto trade-off between fairness and efficiency.

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 utility through multi-objective optimization during sequential rollout. We first formalize fairness in the dynamic setting via stepwise loss functions for Sharing Incentive, Envy Freeness, and Dynamic Pareto Optimality, enabling differentiable training. Leveraging non-wastefulness, we parameterized the solutions by constraining allocations to the subspace of demand while allowing elastic over-allocation when resources remain available. Empirical results demonstrate that our learned allocator achieves substantially higher utility at comparable levels of fairness, uncovering clear Pareto-frontier-like tradeoffs across metrics.

Problem

• Dynamic multi-resource allocation
• Sequential demand
• Fairness
• System utility
• Incompatible fairness criteria

Innovation

• neural allocation mechanism
• dynamic fairness
• multi-objective optimization
• differentiable fairness loss
• elastic over-allocation