# CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency

arXiv: https://arxiv.org/abs/2302.08681 · HTML: https://mlsanity.com/paper/2302.08681/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2023.

## Built on by (most cited first, 17 of 17)

- [Power Hungry Processing: Watts Driving the Cost of AI Deployment?](https://mlsanity.com/paper/2311.16863/) (2023)
- [Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms](https://mlsanity.com/paper/2310.20598/) (2023)
- [Scalability Optimization in Cloud-Based AI Inference Services: Strategies for Real-Time Load Balancing and Automated Scaling](https://mlsanity.com/paper/2504.15296/) (2025)
- [EnsembleCI: Ensemble Learning for Carbon Intensity Forecasting](https://mlsanity.com/paper/2505.01959/) (2025)
- [Chasing Convex Functions with Long-term Constraints](https://mlsanity.com/paper/2402.14012/) (2024)
- [Uncertainty-Aware Decarbonization for Datacenters](https://mlsanity.com/paper/2407.02390/) (2024)
- [LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand](https://mlsanity.com/paper/2404.15211/) (2024)
- [PowerSlider: Exploiting Phase Asymmetry for LLM Serving under Demand Response](https://mlsanity.com/paper/2608.21719/) (2026)
- [The Hidden AI Race: Tracking Environmental Costs of Innovation](https://mlsanity.com/paper/2511.22781/) (2025)
- [XWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms](https://mlsanity.com/paper/2605.23348/) (2026)
- [MAIZX: A Carbon-Aware Framework for Optimizing Cloud Computing Emissions](https://mlsanity.com/paper/2506.19972/) (2025)
- [Signal-Aware Workload Shifting Algorithms with Uncertainty-Quantified Predictors](https://mlsanity.com/paper/2509.26511/) (2025)
- [Distributed LLM Pretraining During Renewable Curtailment Windows: A Feasibility Study](https://mlsanity.com/paper/2602.22760/) (2026)
- [Green or Fast? Learning to Balance Cold Starts and Idle Carbon in Serverless Computing](https://mlsanity.com/paper/2602.23935/) (2026)
- [FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models](https://mlsanity.com/paper/2604.16448/) (2026)
- [SLA-Constrained Carbon-Aware Routing in Geo-Distributed Serverless Clouds](https://mlsanity.com/paper/2607.22806/) (2026)
- [Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving](https://mlsanity.com/paper/2610.14571/) (2026)

## Abstract (arXiv)

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.