To improve the environmental implications of the growing demand of computing, future applications need to improve the carbon-efficiency of computing infrastructures. State-of-the-art approaches, however, do not consider the intermittent nature of renewable energy. The time and location-based carbon intensity of energy fueling computing has been ignored when determining how computation is carried out. This poses a new challenge -- deciding when and where to run applications across consumer devices at the edge and servers in the cloud. Such scheduling decisions become more complicated with the stochastic runtime variance and the amortization of the rising embodied emissions. This work proposes GreenScale, a framework to understand the design and optimization space of carbon-aware scheduling for green applications across the edge-cloud infrastructure. Based on the quantified carbon output of the infrastructure components, we demonstrate that optimizing for carbon, compared to performance and energy efficiency, yields unique scheduling solutions. Our evaluation with three representative categories of applications (i.e., AI, Game, and AR/VR) demonstrate that the carbon emissions of the applications can be reduced by up to 29.1% with the GreenScale. The analysis in this work further provides a detailed road map for edge-cloud application developers to build green applications.

翻译：为改善日益增长的算力需求对环境的影响，未来应用需提升计算基础设施的碳效率。然而，现有方法未考虑可再生能源的间歇性特征。在确定计算执行方式时，能源供给的时空碳强度差异长期被忽略。这带来全新的挑战——如何在边缘端消费设备与云端服务器之间，决定应用的运行时机与位置。这种调度决策因随机运行时波动及隐含排放的摊销问题而愈发复杂。本文提出GreenScale框架，旨在理解面向边缘-云基础设施的绿色应用在碳感知调度中的设计与优化空间。基于基础设施组件的量化碳排放量，我们证明：相较于性能和能效优化，针对碳排放的优化会产生独特的调度方案。通过三类代表性应用（AI、游戏、AR/VR）的评估，GreenScale可实现最高29.1%的碳排放降低。本文的分析进一步为边缘-云应用开发者构建绿色应用提供了详细路线图。