Going Green for Less Green: Optimizing the Cost of Reducing Cloud Carbon Emissions
Name
3620666.3651374.pdf
Size
2.05 MB
Format
Adobe PDF
Checksum (MD5)
6e56904d8495ae5f958b04a9e465a80c
Author(s)
Hanafy, Walid A.
Liang, Qianlin
Bashir, Noman
Souza, Abel
Irwin, David
Shenoy, Prashant
Date Issued
April 27, 2024
Publisher
ACM
Citation
Hanafy, Walid A., Liang, Qianlin, Bashir, Noman, Souza, Abel, Irwin, David et al. 2024. "Going Green for Less Green: Optimizing the Cost of Reducing Cloud Carbon Emissions."
Version
Final published version
Abstract
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%.
Description
ASPLOS '24: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems April 27-May 1, 2024, La Jolla, CA, USA
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3620666.3651374
