A Survey on Caching Systems in Serverless Computing

Abstract

Serverless computing, particularly Function-as-aService (FaaS), has emerged as a dominant cloud computing paradigm offering automatic scaling, pay-per-use pricing, and simplified deployment. However, the stateless and ephemeral nature of serverless functions introduces significant performance challenges, most notably cold-start latency and costly remote data access. Caching has been widely recognized as a key technique to mitigate these issues, yet the unique constraints of serverless environments-such as ephemeral containers, limited local state, and multi-tenant resource sharing-demand fundamentally new caching designs. In this paper, we present a concise survey of five representative caching systems for serverless platforms published between 2021 and 2025. We categorize them into three classes: container-level caching for cold-start mitigation, datalevel caching for reducing storage access latency, and cache coherence mechanisms for distributed consistency. We compare these systems along key dimensions including caching granularity, eviction strategy, transparency, and performance gains. Finally, we identify open challenges and promising directions for future research.