自定义保持活动缓存策略
Custom Keep-Alive Cache Policies
AI总结：
本文提出了一种基于在线学习的自定义缓存分配策略，通过客户投标实现缓存缺失成本的动态分配，并通过两种支付方案保证激励和收益属性。
摘要：
我们研究了适用于无服务器计算的保持活动缓存策略的市场设计。先前的工作假设缓存缺失的成本（冷启动）在所有客户应用程序之间是均匀的。然而，缓存缺失的成本取决于客户的应用程序。我们研究了客户提交其缓存缺失成本投标的市场设计。我们基于混合固定分配专家的在线学习设计了缓存分配策略。我们证明了我们的自定义缓存分配策略在渐近上是有效的，并且随着提交的投标单调非递增。我们考察了两种收费客户的方法以实现良好的激励效果。在第一种支付方案中，客户根据Myerson的理论进行收费，而在第二种支付方案中，客户根据其外部性进行收费。我们通过混合模拟和理论证明，两种方案都具有理想的收入和激励属性。
英文摘要：
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.
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