Major innovations in computing have been driven by scaling up computing infrastructure, while aggressively optimizing operating costs. The result is a network of worldwide datacenters that consume a large amount of energy, mostly in an energy-efficient manner. Since the electric grid powering these datacenters provided a simple and opaque abstraction of an unlimited and reliable power supply, the computing industry remained largely oblivious to the carbon intensity of the electricity it uses. Much like the rest of the society, it generally treated the carbon intensity of the electricity as constant, which was mostly true for a fossil fuel-driven grid. As a result, the cost-driven objective of increasing energy-efficiency -- by doing more work per unit of energy -- has generally been viewed as the most carbon-efficient approach. However, as the electric grid is increasingly powered by clean energy and is exposing its time-varying carbon intensity, the most energy-efficient operation is no longer necessarily the most carbon-efficient operation. There has been a recent focus on exploiting the flexibility of computing's workloads -- along temporal, spatial, and resource dimensions -- to reduce carbon emissions, which comes at the cost of either performance or energy efficiency. In this paper, we discuss the trade-offs between energy efficiency and carbon efficiency in exploiting computing's flexibility and show that blindly optimizing for energy efficiency is not always the right approach.

翻译：计算领域的重大创新源于对计算基础设施的大规模扩展，同时积极优化运营成本。其结果是全球范围内庞大的数据中心网络消耗大量能源，且多以高能效方式运行。由于为这些数据中心供电的电网提供了一个简单且不透明的抽象——即无限可靠的电能供应，计算行业在很大程度上忽视了所用电力中蕴含的碳强度。与社会其他领域类似，该行业通常将电力的碳排放强度视为恒定值——这在以化石燃料为主的电网中基本成立。因此，以单位能量完成更多工作来提升能效的成本驱动目标，通常被视为最具碳效率的途径。然而，随着电网越来越多地由清洁能源驱动，并暴露出其随时间变化的碳强度特征，最高能效的运行方式不再必然等同于最高碳效率的运行方式。近期研究开始关注利用计算工作负载的灵活性（时间、空间和资源维度）来减少碳排放，但这种灵活性的实现往往以性能或能效为代价。本文探讨了利用计算灵活性时能效与碳效率之间的权衡关系，并表明盲目追求能效优化并非始终是正确路径。