Online Container Caching for IoT Data Processing in Serverless Edge Computing

Serverless edge computing is an efficient way to execute event-driven, short-duration, and bursty IoT data processing tasks on resource-limited edge servers, using on-demand resource allocation and dynamic auto-scaling. In this paradigm, function requests are handled in virtualized environments, e.g., containers. When a function request arrives online, if there is no container in memory to execute it, the serverless platform will initialize such a container with non-negligible latency, known as cold start. Otherwise, it results in a warm start with no latency in previous studies. However, based on our experiments, we find there is a remarkable third case called Late-Warm, i.e., when a request arrives during the container initializing, its latency is less than a cold start but not zero. In this paper, we study online container caching in serverless edge computing to minimize the total latency with Late-Warm and other practical issues considered. We propose OnCoLa, a novel $O(T_{c}K)$-competitive algorithm supporting request relaying on multiple edge servers. Here, $T_{c}$ and $K$ are the maximum container cold start latency and the memory size, respectively. Extensive simulations on two real-world traces demonstrate that OnCoLa consistently outperforms the state-of-the-art container caching algorithms and reduces the latency by $23.33\%$. Experiments on Raspberry Pi and Jetson Nano show that OnCoLa reduces latency by up to $21.38\%$ compared with the representative lightweight policy.