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

arXiv: https://arxiv.org/abs/2602.23935 · HTML: https://mlsanity.com/paper/2602.23935/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 27 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider](https://mlsanity.com/paper/2003.03423/) (2020)
- [CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency](https://mlsanity.com/paper/2302.08681/) (2023)
- [Clover: Toward Sustainable AI with Carbon-Aware Machine Learning Inference Service](https://mlsanity.com/paper/2304.09781/) (2023)
- [On the Role of Discount Factor in Offline Reinforcement Learning](https://mlsanity.com/paper/2206.03383/) (2022)
- [Serverless Computing: A Survey of Opportunities, Challenges and Applications](https://mlsanity.com/paper/1911.01296/) (2019)
- [Serverless Cold Starts and Where to Find Them](https://mlsanity.com/paper/2410.06145/) (2024)
- [FaaSLight: General Application-Level Cold-Start Latency Optimization for Function-as-a-Service in Serverless Computing](https://mlsanity.com/paper/2207.08175/) (2022)
- [CASA: A Framework for SLO and Carbon-Aware Autoscaling and Scheduling in Serverless Cloud Computing](https://mlsanity.com/paper/2409.00550/) (2024)
- [EcoLife: Carbon-Aware Serverless Function Scheduling for Sustainable Computing](https://mlsanity.com/paper/2409.02085/) (2024)
- [SPES: Towards Optimizing Performance-Resource Trade-Off for Serverless Functions](https://mlsanity.com/paper/2403.17574/) (2024)
- [Dirigent: Lightweight Serverless Orchestration](https://mlsanity.com/paper/2404.16393/) (2024)
- [Prediction-driven resource provisioning for serverless container runtimes](https://mlsanity.com/paper/2410.19215/) (2024)

## Abstract (arXiv)

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.