Tackling Cold Start in Serverless Computing with Multi-Level Container Reuse

Amelie Chi Zhou, Rongzheng Huang, Zhoubin Ke, Yusen Li, Yi Wang, Rui Mao

Abstract: In Serverless Computing, function cold-start is a major issue that causes delay of the system. Various solutions have been proposed to address function cold-start issue, among which keeping containers alive after function completion is an easy and commonly adopted way in real serverless clouds. However, when reusing warm containers for function warm starts, existing systems only match functions to containers with the same configurations. This greatly limits the warm resource utilization. Our analysis of real-world applications reveals that many serverless applications share the same operating system and language frameworks. Thus, we propose multi-level container reuse that tries to reduce the startup latency of functions using "similar" containers to greatly improve warm resource utilization. Due to the complexity of selecting the best container reuse solutions, we designed a Deep Reinforcement Learning (DRL) based scheduler to efficiently and effectively address the problem. Moreover, we released a new serverless benchmark named FStartBench that contains detailed package information for comparing the effectiveness of different function cold-start methods. Experiments based on FStartBench show that, given a warm resource pool with fixed size, our DRL-based scheduler can achieve up to 53% reduction on the average function startup latency compared to state-of-the-art solutions.

External IDs:dblp:conf/ipps/ZhouHKLW024