CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency
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Cloud platforms are increasingly emphasizing sustainable operations in order to reduce their operational carbon footprint. One approach for reducing emissions is to exploit the temporal flexibility inherent in many cloud workloads by executing them in time periods with the greenest electricity supply 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 workload 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's server allocations are varied dynamically based on fluctuations in the carbon cost of the grid's electricity supply. We present an optimal greedy algorithm for minimizing a job's emissions through carbon scaling and implement a prototype of our system in Kubernetes using its autoscaling capabilities, along with an analytic tool to guide the carbon-efficient deployment of batch applications in the cloud. We evaluate CarbonScaler using real-world machine learning training and MPI jobs on a commercial cloud platform and show that can yield up to 50% carbon savings over a carbon agnostic execution and up to 35 state-of-the-art suspend resume policies.
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