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arXiv 2209.07035cs.GT
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面向带供应成本与容量限制的资源分配的在线组合拍卖

Online Combinatorial Auctions for Resource Allocation with Supply Costs and Capacity Limits

Xiaoqi Tan, Alberto Leon-Garcia, Yuan Wu, Danny H. K. Tsang

AI总结:

针对带供应成本与容量限制的在线资源分配问题,提出具备最优竞争比的挂牌价拍卖机制,经云计算场景验证,该机制竞争力与鲁棒性优于现有基准。

摘要:

我们研究算法机制设计中的一类通用在线组合拍卖问题。服务提供商将多种容量受限的资源分配给以任意顺序陆续到达的客户。每位客户对其可购买的资源束(例如云计算中CPU与RAM等不同资源的组合)持有私有估值函数。提供商会向购买资源束的客户收取费用,且其供应成本会随已分配资源的总量递增。研究目标是最大化社会福利,即客户对所购资源束的总估值,减去提供商为所有已分配资源付出的总供应成本。我们采用竞争分析框架,提出了具备最优竞争比的 posted-price(挂牌价)机制。我们的定价机制具有最优性,即不存在其他在线算法能实现更优的竞争比。我们通过对云计算场景下在线资源分配的实证研究验证了理论结果。数值实验结果表明,所提出的定价机制具备竞争力,对系统不确定性具有鲁棒性,且性能优于现有基准方法。

英文摘要:

We study a general online combinatorial auction problem in algorithmic mechanism design. A provider allocates multiple types of capacity-limited resources to customers that arrive in a sequential and arbitrary manner. Each customer has a private valuation function on bundles of resources that she can purchase (e.g., a combination of different resources such as CPU and RAM in cloud computing). The provider charges payment from customers who purchase a bundle of resources and incurs an increasing supply cost with respect to the totality of resources allocated. The goal is to maximize the social welfare, namely, the total valuation of customers for their purchased bundles, minus the total supply cost of the provider for all the resources that have been allocated. We adopt the competitive analysis framework and provide posted-price mechanisms with optimal competitive ratios. Our pricing mechanism is optimal in the sense that no other online algorithms can achieve a better competitive ratio. We validate the theoretic results via empirical studies of online resource allocation in cloud computing. Our numerical results demonstrate that the proposed pricing mechanism is competitive and robust against system uncertainties and outperforms existing benchmarks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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