The proliferation of latency-critical and compute-intensive edge applications is driving increases in computing demand and carbon emissions at the edge. To better understand carbon emissions at the edge, we analyze granular carbon intensity traces at intermediate "mesoscales," such as within a single US state or among neighboring countries in Europe, and observe significant variations in carbon intensity at these spatial scales. Importantly, our analysis shows that carbon intensity variations, which are known to occur at large continental scales (e.g., cloud regions), also occur at much finer spatial scales, making it feasible to exploit geographic workload shifting in the edge computing context. Motivated by these findings, we propose \proposedsystem, a carbon-aware framework for edge computing that optimizes the placement of edge workloads across mesoscale edge data centers to reduce carbon emissions while meeting latency SLOs. We implement CarbonEdge and evaluate it on a real edge computing testbed and through large-scale simulations for multiple edge workloads and settings. Our experimental results on a real testbed demonstrate that CarbonEdge can reduce emissions by up to 78.7\% for a regional edge deployment in central Europe. Moreover, our CDN-scale experiments show potential savings of 49.5\% and 67.8\% in the US and Europe, respectively, while limiting the one-way latency increase to less than 5.5 ms.

翻译：延迟敏感型和计算密集型边缘应用的激增正推动边缘计算需求及碳排放的增长。为深入理解边缘碳排放，我们分析了中间“中尺度”（如美国单个州内或欧洲相邻国家间）的细粒度碳强度轨迹，并观察到这些空间尺度上存在显著的碳强度差异。重要的是，我们的分析表明，已知在大陆尺度（如云区域）发生的碳强度变化，在更精细的空间尺度上同样存在，这使得在边缘计算场景中利用地理工作负载转移成为可能。基于这些发现，我们提出\proposedsystem——一个面向边缘计算的碳感知框架，通过优化中尺度边缘数据中心间的工作负载布局，在满足延迟服务等级目标的同时降低碳排放。我们实现了CarbonEdge，并在真实边缘计算测试平台及大规模模拟环境中针对多种边缘工作负载和配置进行评估。真实测试平台上的实验结果表明，CarbonEdge在中欧区域边缘部署场景中可降低高达78.7%的碳排放。此外，我们的CDN规模实验显示，在美国和欧洲分别可实现49.5%和67.8%的潜在减排，同时将单向延迟增长控制在5.5毫秒以内。