This paper concerns the mechanism design for online resource allocation in a strategic setting. In this setting, a single supplier allocates capacity-limited resources to requests that arrive in a sequential and arbitrary manner. Each request is associated with an agent who may act selfishly to misreport the requirement and valuation of her request. The supplier charges payment from agents whose requests are satisfied, but incurs a load-dependent supply cost. The goal is to design an incentive compatible online mechanism, which determines not only the resource allocation of each request, but also the payment of each agent, so as to (approximately) maximize the social welfare (i.e., aggregate valuations minus supply cost). We study this problem under the framework of competitive analysis. The major contribution of this paper is the development of a unified approach that achieves the best-possible competitive ratios for setups with different supply costs. Specifically, we show that when there is no supply cost or the supply cost function is linear, our model is essentially a standard 0-1 knapsack problem, for which our approach achieves logarithmic competitive ratios that match the state-of-the-art (which is optimal). For the more challenging setup when the supply cost is strictly-convex, we provide online mechanisms, for the first time, that lead to the optimal competitive ratios as well. To the best of our knowledge, this is the first approach that unifies the characterization of optimal competitive ratios in online resource allocation for different setups including zero, linear and strictly-convex supply costs.
翻译:本文涉及在战略背景下在线资源分配的机制设计。 在这一背景下,单个供应商将能力有限的资源分配给以顺序和任意方式提出的请求。 每份请求都与可能自私地误报要求和对其请求的估价的代理人有关。 供应商向请求得到满足的代理人收取费用,但需要承担一个依赖负荷的供应费用。 目标是设计一个奖励性兼容的在线机制,不仅确定每项请求的资源分配,而且确定每个代理商的付款,以便(约)最大限度地提高社会福利(即总价值减去供应费用)的竞争力(即总价值减去供应费用)。 我们在竞争分析框架内研究这一问题。本文件的主要贡献是制定一种统一的方法,为不同供应成本的配置实现最佳竞争比率。 具体地说,我们表明,当没有供应成本或供应成本结构功能是线性时,我们的模式基本上是一个标准0-1 knappsack 问题,为此,我们的方法可以实现与最新水平(即总价值减去供应成本)相符的对等竞争性竞争比率(这是第一个最理想的时间) 。对于最具有挑战性的在线调配机制来说,这是我们确定最佳供应成本时,最有挑战性地确定最佳的在线领先机制。