The War of the Efficiencies: Understanding the Tension between Carbon and Energy Optimization
The War of the Efficiencies: Understanding the Tension between Carbon and Energy Optimization
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效率之战：了解碳与能源优化之间的紧张关系
DOI：
10.1145/3604930.3605709
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Shenoy, Prashant
中科院分区：
文献类型：
--
作者：
Hanafy, Walid A.;Bostandoost, Roozbeh;Bashir, Noman;Irwin, David;Hajiesmaili, Mohammad;Shenoy, Prashant
计算领域的重大创新是通过扩大计算基础设施规模，同时积极优化运营成本来推动的。其结果是一个世界范围内的网络，消耗大量的能源，主要是在能源效率的方式。由于为这些中心供电的电网提供了一个简单而不透明的无限可靠的电力供应的抽象概念，计算行业在很大程度上仍然忽视了它所使用的电力的碳强度。就像社会的其他部分一样，它通常将电力的碳强度视为常数，这对于化石燃料驱动的电网来说基本上是正确的。因此，成本驱动的提高能源效率的目标-通过每单位能源做更多的工作-通常被视为最具碳效率的方法。然而，随着电网越来越多地由清洁能源供电，并暴露出其随时间变化的碳强度，最节能的操作不再一定是最碳效率的操作。然而，最近的重点是利用计算工作负载的灵活性-沿着时间、空间和资源维度-来减少碳排放，这是以性能或能源效率为代价的。在本文中，我们量化了利用计算灵活性时能源效率和碳效率之间的权衡，并表明盲目优化能源效率并不总是正确的方法。
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. However, the recent focus on exploiting the flexibility of computing's workloads---along temporal, spatial, and resource dimensions---to reduce carbon emissions, comes at the cost of either performance or energy efficiency. In this paper, we quantify 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.1
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