云服务的最优定价：需求不确定下的承诺消费
Optimal Pricing of Cloud Services: Committed Spend under Demand Uncertainty
AI总结：
针对需求不确定下的云服务定价问题，将经典序贯筛选框架扩展到非线性多单位场景，刻画了最优动态合同，阐明其通过两部收费制和承诺消费合同的实现方式，并分析了承诺成本和现货市场对合同的影响。
摘要：
我们考虑一个向具有多单位需求的买家提供服务的卖家。在需求实现之前，买家会收到其未来需求的噪声信号，卖家可根据该信号的报告值设计合同。因此，买家可与服务提供商签订未来未知消费水平的合同，例如在云计算资源或软件服务市场中。我们刻画了最优动态合同，将经典的序贯筛选框架扩展到非线性多单位环境。最优机制为报告更高信号的买家提供折扣，但作为交换，他们必须支付更大的固定费用。随后，我们描述了最优机制如何通过实践中常见的两种合同形式（两部收费制和承诺消费合同）实现。最后，我们通过扩展基础模型来阐明政策相关问题，例如分析当买家面临承诺成本或存在流动性现货市场时，最优合同会如何变化。
英文摘要：
We consider a seller who offers services to a buyer with multi-unit demand. Prior to the realization of demand, the buyer receives a noisy signal of their future demand, and the seller can design contracts based on the reported value of this signal. Thus, the buyer can contract with the service provider for an unknown level of future consumption, such as in the market for cloud computing resources or software services. We characterize the optimal dynamic contract, extending the classic sequential screening framework to a nonlinear and multi-unit setting. The optimal mechanism gives discounts to buyers who report higher signals, but in exchange they must provide larger fixed payments. We then describe how the optimal mechanism can be implemented by two common forms of contracts observed in practice, the two-part tariff and the committed spend contract. Finally, we use extensions of our base model to shed light on policy-focused questions, such as analyzing how the optimal contract changes when the buyer faces commitment costs, or when there are liquid spot markets.
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。
