CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency

Understand

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.