CarbonEdge: Leveraging Mesoscale Spatial Carbon-Intensity Variations for Low Carbon Edge Computing

2025-02-19

AI Summary

The coexistence of rapidly increasing carbon emissions from edge computing and stringent latency requirements for delay-sensitive applications poses a critical sustainability challenge. Method: This paper introduces the first geography-aware, carbon-aware edge scheduling framework that exploits previously unutilized mesoscale (state- or neighboring-country-level) spatial heterogeneity in carbon intensity—moving beyond conventional intercontinental or temporal optimization paradigms. It integrates spatiotemporal carbon intensity modeling with multi-objective integer programming under strict latency constraints. Contribution/Results: Evaluated on a real-world edge testbed and large-scale CDN simulations, the framework achieves up to 78.7% carbon reduction for edge deployments in Central Europe, and 49.5% and 67.8% reductions in U.S. and European CDN scenarios, respectively, while introducing less than 5.5 ms one-way latency overhead. This work delivers the first systematic, fine-grained geographic scheduling solution enabling low-carbon edge computing.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Responsible Web: Sustainability and climate impact of web technologies
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Reduce carbon emissions in edge computing
• Optimize workload placement across edge data centers
• Exploit mesoscale spatial carbon-intensity variations

Innovation

• Mesoscale spatial carbon-intensity analysis
• Carbon-aware edge workload placement optimization
• Geographic workload shifting for emission reduction