Custom Keep-Alive Cache Policies

TL;DR

This paper introduces a market-based approach for custom keep-alive cache policies in serverless computing, optimizing cache allocation based on customer bids and ensuring incentive compatibility.

Contribution

It proposes a novel cache allocation policy using online learning that accounts for customer-specific cache miss costs and analyzes two incentive-compatible charging schemes.

Findings

The proposed policy is asymptotically efficient.

Both payment schemes yield good revenue and incentive properties.

The approach adapts to customer bids for personalized cache management.

Abstract

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.