[2602.23935] Green or Fast? Learning to Balance Cold Starts and Idle Carbon in Serverless Computing

Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2602.23935 (cs) [Submitted on 27 Feb 2026] Title:Green or Fast? Learning to Balance Cold Starts and Idle Carbon in Serverless Computing Authors:Bowen Sun, Christos D. Antonopoulos, Evgenia Smirni, Bin Ren, Nikolaos Bellas, Spyros Lalis View a PDF of the paper titled Green or Fast? Learning to Balance Cold Starts and Idle Carbon in Serverless Computing, by Bowen Sun and 5 other authors View PDF HTML (experimental) Abstract:Serverless computing simplifies cloud deployment but introduces new challenges in managing service latency and carbon emissions. Reducing cold-start latency requires retaining warm function instances, while minimizing carbon emissions favors reclaiming idle resources. This balance is further complicated by time-varying grid carbon intensity and varying workload patterns, under which static keep-alive policies are inefficient. We present LACE-RL, a latency-aware and carbon-efficient management framework that formulates serverless pod retention as a sequential decision problem. LACE-RL uses deep reinforcement learning to dynamically tune keep-alive durations, jointly modeling cold-start probability, function-specific latency costs, and real-time carbon intensity. Using the Huawei Public Cloud Trace, we show that LACE-RL reduces cold starts by 51.69% and idle keep-alive carbon emissions by 77.08% compared to Huawei's static policy, while achieving better latency-carbon trade-offs than stat...