# LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand

arXiv: https://arxiv.org/abs/2404.15211 · HTML: https://mlsanity.com/paper/2404.15211/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 64 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 24 of 24)

- [Competitive caching with machine learned advice](https://mlsanity.com/paper/1802.05399/) (2018)
- [Power Hungry Processing: Watts Driving the Cost of AI Deployment?](https://mlsanity.com/paper/2311.16863/) (2023)
- [Measuring the Carbon Intensity of AI in Cloud Instances](https://mlsanity.com/paper/2206.05229/) (2022)
- [Chasing Carbon: The Elusive Environmental Footprint of Computing](https://mlsanity.com/paper/2011.02839/) (2020)
- [Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems](https://mlsanity.com/paper/2109.01556/) (2021)
- [CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency](https://mlsanity.com/paper/2302.08681/) (2023)
- [Competitive Algorithms for the Online Multiple Knapsack Problem with Application to Electric Vehicle Charging](https://mlsanity.com/paper/2010.00412/) (2020)
- [Data-driven Competitive Algorithms for Online Knapsack and Set Cover](https://mlsanity.com/paper/2012.05361/) (2020)
- [Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud](https://mlsanity.com/paper/2110.13234/) (2021)
- [Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms](https://mlsanity.com/paper/2310.20598/) (2023)
- [Online Search with Predictions: Pareto-optimal Algorithm and its Applications in Energy Markets](https://mlsanity.com/paper/2211.06567/) (2022)
- [The Online Pause and Resume Problem: Optimal Algorithms and An Application to Carbon-Aware Load Shifting](https://mlsanity.com/paper/2303.17551/) (2023)
- [GreenScale: Carbon-Aware Systems for Edge Computing](https://mlsanity.com/paper/2304.00404/) (2023)
- [Optimal Posted Prices for Online Cloud Resource Allocation](https://mlsanity.com/paper/1704.05511/) (2017)
- [On the Limitations of Carbon-Aware Temporal and Spatial Workload Shifting in the Cloud](https://mlsanity.com/paper/2306.06502/) (2023)
- [CASPER: Carbon-Aware Scheduling and Provisioning for Distributed Web Services](https://mlsanity.com/paper/2403.14792/) (2024)
- [Ecovisor: A Virtual Energy System for Carbon-Efficient Applications](https://mlsanity.com/paper/2210.04951/) (2022)
- [Junkyard Computing: Repurposing Discarded Smartphones to Minimize Carbon](https://mlsanity.com/paper/2110.06870/) (2021)
- [The Dirty Secret of SSDs: Embodied Carbon](https://mlsanity.com/paper/2207.10793/) (2022)
- [The War of the Efficiencies: Understanding the Tension between Carbon and Energy Optimization](https://mlsanity.com/paper/2306.16948/) (2023)
- [Sustainable Computing -- Without the Hot Air](https://mlsanity.com/paper/2207.00081/) (2022)
- [The Online Knapsack Problem with Departures](https://mlsanity.com/paper/2209.11934/) (2022)
- [Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks](https://mlsanity.com/paper/2301.06087/) (2023)
- [Carbon Containers: A System-level Facility for Managing Application-level Carbon Emissions](https://mlsanity.com/paper/2309.14477/) (2023)

## Built on by (most cited first, 2 of 2)

- [Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints](https://mlsanity.com/paper/2408.07831/) (2024)
- [Signal-Aware Workload Shifting Algorithms with Uncertainty-Quantified Predictors](https://mlsanity.com/paper/2509.26511/) (2025)

## Abstract (arXiv)

Motivated by an imperative to reduce the carbon emissions of cloud data centers, this paper studies the online carbon-aware resource scaling problem with unknown job lengths (OCSU) and applies it to carbon-aware resource scaling for executing computing workloads. The task is to dynamically scale resources (e.g., the number of servers) assigned to a job of unknown length such that it is completed before a deadline, with the objective of reducing the carbon emissions of executing the workload. The total carbon emissions of executing a job originate from the emissions of running the job and excess carbon emitted while switching between different scales (e.g., due to checkpoint and resume). Prior work on carbon-aware resource scaling has assumed accurate job length information, while other approaches have ignored switching losses and require carbon intensity forecasts. These assumptions prohibit the practical deployment of prior work for online carbon-aware execution of scalable computing workload. We propose LACS, a theoretically robust learning-augmented algorithm that solves OCSU. To achieve improved practical average-case performance, LACS integrates machine-learned predictions of job length. To achieve solid theoretical performance, LACS extends the recent theoretical advances on online conversion with switching costs to handle a scenario where the job length is unknown. Our experimental evaluations demonstrate that, on average, the carbon footprint of LACS lies within 1.2% of the online baseline that assumes perfect job length information and within 16% of the offline baseline that, in addition to the job length, also requires accurate carbon intensity forecasts. Furthermore, LACS achieves a 32% reduction in carbon footprint compared to the deadline-aware carbon-agnostic execution of the job.