Online Resource Allocation

Overview

Online resource allocation asks how to commit limited resources to requests that arrive over time, when future demand and rewards may be unknown. Methods include linear-programming and primal-dual pricing, threshold and greedy policies, and learning approaches such as bandits and reinforcement learning; applications range from inventory and queues to cloud and language-model serving. The goal is often to maximize welfare or revenue while respecting capacity, budget, deadline, or fairness constraints. Theory can provide constant or sublinear regret under suitable assumptions, but handling nonstationary demand, degenerate constraints, unreliable predictions, and hard feasibility guarantees together remains challenging.

Latest papers

CardsList

96 papers9 papers in the last four weeks, up 13% on the four weeks before. 0.1% of all new papers.

  1. Power- and Fragmentation-aware Online Scheduling for GPU Datacenters

    Dec 23, 2024Francesco Lettich, Emanuele Carlini, Franco Maria Nardini +2Online Resource AllocationData Center Energy Management

  2. Allocation Stability and Wald Inference under Variance-Aware UCB

    Dec 12, 2024Yingying Fan, Yuxuan Han, Jinchi Lv +2Multi-Armed BanditsUCB Algorithms

  3. Infrequent Resolving Algorithm for Online Linear Programming

    Aug 1, 2024Guokai Li, Zizhuo Wang, Jingwei ZhangSequential Decision MakingRegret Minimization

  4. Online Linear Programming with Batching

    Aug 1, 2024Haoran Xu, Peter W. Glynn, Yinyu YeSequential Decision MakingRegret Minimization

  5. Learning to Schedule in Parallel-Server Queues with Stochastic Bilinear Rewards

    Dec 13, 2021Jung-hun Kim, Milan VojnovicMulti-Armed BanditsOnline Resource Allocation

  6. Equity Promotion in Online Resource Allocation

    Dec 8, 2021Pan Xu, Yifan XuSequential Decision MakingFair Resource Allocation