We study the market design of keep-alive caching policies applicable in serverless computing. Prior work has assumed that the cost of a cache miss (cold start) is uniform across all customer applications. However, the cost of a cache miss depends on the customer's application. We investigate the market design where the customers submit a bid for their cost of a cache miss. We design a cache allocation policy based on online learning from a mixture of fixed allocation experts. We show that our custom cache allocation policy is asymptotically efficient and monotonically non-increasing with respect to the submitted bid. We examine two ways of charging customers to achieve good incentives. In the first payment scheme the customers are charged based on Myerson's theory, whereas in the second payment scheme the customers are charged their externality. We show via a mix of simulations and theory that both schemes have desirable revenue and incentive properties.

翻译：本文研究了适用于无服务器计算的保活缓存策略的市场设计问题。先前研究假设缓存未命中（冷启动）的成本在所有客户应用中是一致的。然而，缓存未命中的成本实际上取决于客户的具体应用。我们探讨了一种市场设计机制，允许客户为其缓存未命中成本提交报价。基于混合固定分配专家的在线学习，我们设计了一种缓存分配策略。研究表明，该定制化缓存分配策略具有渐近有效性，且相对于客户提交的报价呈现单调非递增特性。为建立良好激励机制，我们考察了两种客户计费方式：第一种计费方案基于迈尔森理论，第二种则依据客户产生的外部性进行收费。通过仿真与理论分析相结合的方法，我们证明两种方案均具备理想的收益与激励特性。