Retention-Aware Container Caching for Serverless Edge Computing

Serverless edge computing adopts an event-based model where Internet-of-Things (IoT) services are executed in lightweight containers only when requested, leading to significantly improved edge resource utilization. Unfortunately, the startup latency of containers degrades the responsiveness of IoT services dramatically. Container caching, while masking this latency, requires retaining resources thus compromising resource efficiency. In this paper, we study the retention-aware container caching problem in serverless edge computing. We leverage the distributed and heterogeneous nature of edge platforms and propose to optimize container caching jointly with request distribution. We reveal step by step that this joint optimization problem can be mapped to the classic ski-rental problem. We first present an online competitive algorithm for a special case where request distribution and container caching are based on a set of carefully designed probability distribution functions. Based on this algorithm, we propose an online algorithm called O-RDC for the general case, which incorporates the resource capacity and network latency by opportunistically distributing requests. We conduct extensive experiments to examine the performance of the proposed algorithms with both synthetic and real-world serverless computing traces. Our results show that O-RDC outperforms existing caching strategies of current serverless computing platforms by up to 94.5% in terms of the overall system cost.