一句话：提出碳感知调度器 GAIA，在云批处理作业中权衡碳排、性能与成本，显著提升碳节省效率并降低性能开销。
云数据中心容量增长使大规模计算工作负载的碳排放受到关注。云用户常将批处理作业时序迁移到碳强度低的时段，但这会延长完成时间，并因周期性闲置资源而改变需求模式，削弱预留实例等购买选项带来的成本节省，从而形成碳排、性能与成本的三方权衡。本文提出碳感知调度器 GAIA，使用户能在云批处理调度中处理该三方权衡。作者量化了云平台中的碳排-性能-成本权衡，并表明相比现有碳感知调度策略，所提策略在成本每增加一个百分点时可带来两倍的碳节省，同时将性能开销降低 26%。
The continued exponential growth of cloud datacenter capacity has increased awareness of the carbon emissions when executing large compute-intensive workloads. To reduce carbon emissions, cloud users often temporally shift their batch workloads to periods with low carbon intensity. While such time shifting can increase job completion times due to their delayed execution, the cost savings from cloud purchase options, such as reserved instances, also decrease when users operate in a carbon-aware manner. This happens because carbon-aware adjustments change the demand pattern by periodically leaving resources idle, which creates a trade-off between carbon emissions and cost. In this paper, we present GAIA, a carbon-aware scheduler that enables users to address the three-way trade-off between carbon, performance, and cost in cloud-based batch schedulers. Our results quantify the carbon-performance-cost trade-off in cloud platforms and show that compared to existing carbon-aware scheduling policies, our proposed policies can double the amount of carbon savings per percentage increase in cost, while decreasing the performance overhead by 26%.
@inproceedings{HanafyLBSIS24,
title = {Going Green for Less Green: Optimizing the Cost of Reducing Cloud Carbon Emissions},
author = {Walid A. Hanafy and Qianlin Liang and Noman Bashir and Abel Souza and David Irwin and Prashant Shenoy},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3620666.3651374}
}