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.

翻译：动态多资源分配是共享计算环境中的核心问题，在此环境中，用户需求顺序到达，且必须在未知未来需求的情况下公平分配资源。现有方法强调公平性保障，如共享激励、无嫉妒性和动态帕累托最优性，但往往忽略系统效用。此外，这些公平性准则互不相容，无法同时严格实现。我们提出一种神经分配机制，通过顺序展开中的多目标优化调和公平性与效用。我们首先利用逐步损失函数形式化动态场景中的共享激励、无嫉妒性和动态帕累托最优性，从而实现可微训练。基于非浪费性，我们通过将分配约束在需求子空间内来参数化解空间，同时在资源可用时允许弹性过度分配。实验结果表明，我们学习到的分配器在相当公平性水平下实现了显著更高的效用，揭示了各指标间清晰的帕累托前沿类权衡关系。