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

• URL: http://arxiv.org/abs/2602.23935v1
• Date: Fri, 27 Feb 2026 11:35:15 GMT
• Title: Green or Fast? Learning to Balance Cold Starts and Idle Carbon in Serverless Computing
• Abstract summary: Serverless computing simplifies cloud deployment but introduces new challenges in managing service latency and carbon emissions.<n>We present LACE-RL, a latency-aware and carbon-efficient management framework.<n>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.
• Score: 12.749575649611643
• License: http://creativecommons.org/licenses/by/4.0/
• 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 state-of-the-art heuristic and single-objective baselines, approaching Oracle performance.

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