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CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency

Published: 12 December 2023 Publication History

Abstract

Cloud platforms are increasing their emphasis on sustainability and reducing their operational carbon footprint. A common approach for reducing carbon emissions is to exploit the temporal flexibility inherent to many cloud workloads by executing them in periods with the greenest energy and suspending them at other times. Since such suspend-resume approaches can incur long delays in job completion times, we present a new approach that exploits the elasticity of batch workloads in the cloud to optimize their carbon emissions. Our approach is based on the notion of "carbon scaling," similar to cloud autoscaling, where a job dynamically varies its server allocation based on fluctuations in the carbon cost of the grid's energy. We develop a greedy algorithm for minimizing a job's carbon emissions via carbon scaling that is based on the well-known problem of marginal resource allocation. We implement a CarbonScaler prototype in Kubernetes using its autoscaling capabilities and an analytic tool to guide the carbon-efficient deployment of batch applications in the cloud. We then evaluate CarbonScaler using real-world machine learning training and MPI jobs on a commercial cloud platform and show that it can yield i) 51% carbon savings over carbon-agnostic execution; ii) 37% over a state-of-the-art suspend-resume policy; and iii) 8 over the best static scaling policy.

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cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems
Proceedings of the ACM on Measurement and Analysis of Computing Systems  Volume 7, Issue 3
POMACS
December 2023
599 pages
EISSN:2476-1249
DOI:10.1145/3637453
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Published: 12 December 2023
Published in POMACS Volume 7, Issue 3

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  1. auto scaling
  2. carbon efficiency
  3. sustainable computing

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