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arXiv Machine Learning · 2026/7/28 04:00:00
SLA-Constrained Carbon-Aware Routing in Geo-Distributed Serverless Clouds
AI 中文解读
核心亮点:最新研究让全球分布的云服务器能像智能导航一样,在保障用户流畅体验的同时,自动选择碳排放更低的“绿色路线”,最高可减少近一半的碳足迹。
通俗解读:云服务商通常把数据放在全球多个机房,传统方式只优先选响应最快的机房,却忽略了这些机房所在地区的电力来源。有些地方用煤电,碳排放高;有些地方用风电、水电,更清洁。这篇论文提出了一套新算法,它会在不牺牲用户体验(比如延迟、响应速度)的前提下,主动把任务路由到更清洁的机房。好比快递公司送包裹,虽然有一条路最快,但另一条路只慢一点点,可碳排放低很多,系统就会自动选择后者。
实际影响:对普通用户来说,刷视频、玩游戏、用云盘的速度几乎感觉不到变化,但背后整个过程更环保了。这项技术能让科技巨头在不降低服务质量的前提下积极参与碳中和,推动清洁能源的使用。它直接贡献联合国可持续发展目标中的气候行动和清洁能源目标,让每个人在享受便捷数字生活的同时,也为地球减碳出一份力。
arXiv:2607.22806v1 Announce Type: new
Abstract: Modern cloud deployments distribute applications across multiple geographic regions, yet standard routing mechanisms prioritize latency while ignoring the fluctuating carbon intensity of local power grids. Latency-driven routing incurs avoidable carbon emissions, particularly when cleaner regions are within acceptable latency bounds. The proposed model formulates the carbon-aware serverless routing problem as a constrained optimization over geo-distributed cloud regions and introduces an SLA-constrained carbon-aware routing policy that achieves optimal carbon reduction within the SLA-feasible region, evaluated using real carbon intensity measurements across 5 primary AWS deployments. Experimental results show that the proposed policy achieves up to 46.8% carbon reduction while maintaining zero SLA violations across all evaluated thresholds. The system reduces carbon by an average of 27.4% under mixed workloads, and the routing overhead is very low (less than 0.02% of total request latency). A scalability study across 12 AWS regions spanning 6 continents demonstrates that average carbon savings increase from 27.4% to 47.5% as routing flexibility expands under mixed workloads. The proposed work contributes to SDG 13 (Climate Action) and SDG 7 (Affordable and Clean Energy) by enabling low-carbon routing decisions. These results indicate that cloud systems can achieve significant carbon savings without compromising user experience.
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