绿色还是快速？学习在无服务器计算中平衡冷启动和空闲碳排放
Green or Fast? Learning to Balance Cold Starts and Idle Carbon in Serverless Computing
AI总结：
LACE-RL通过深度强化学习在无服务器计算中平衡冷启动延迟与碳排放，实现更高效的资源管理和更优的延迟-碳贸易平衡。
摘要：
无服务器计算简化了云部署，但带来了管理服务延迟和碳排放的新挑战。减少冷启动延迟需要保留温暖的功能实例，而最小化碳排放则倾向于回收空闲资源。这种平衡进一步被时间变化的电网碳强度和变化的工作负载模式所复杂化，在这种情况下，静态保持活动策略是低效的。我们提出了LACE-RL，一个具有延迟意识和碳效率的管理框架，将无服务器pod保留视为一个顺序决策问题。LACE-RL使用深度强化学习动态调整保持活动持续时间，联合建模冷启动概率、功能特定的延迟成本和实时碳强度。使用华为公共云跟踪数据，我们显示LACE-RL相比华为的静态策略减少了51.69%的冷启动和77.08%的空闲保持活动碳排放，同时在延迟-碳贸易平衡方面优于最先进的启发式和单目标基线，接近Oracle性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
