# Signal-Aware Workload Shifting Algorithms with Uncertainty-Quantified Predictors

arXiv: https://arxiv.org/abs/2509.26511 · HTML: https://mlsanity.com/paper/2509.26511/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 53 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 25 of 25)

- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Competitive caching with machine learned advice](https://mlsanity.com/paper/1802.05399/) (2018)
- [Measuring the Carbon Intensity of AI in Cloud Instances](https://mlsanity.com/paper/2206.05229/) (2022)
- [Online metric algorithms with untrusted predictions](https://mlsanity.com/paper/2003.02144/) (2020)
- [Improving Online Algorithms via ML Predictions](https://mlsanity.com/paper/2407.17712/) (2024)
- [Global optimization of Lipschitz functions](https://mlsanity.com/paper/1703.02628/) (2017)
- [Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems](https://mlsanity.com/paper/2109.01556/) (2021)
- [Chasing Convex Bodies and Functions with Black-Box Advice](https://mlsanity.com/paper/2206.11780/) (2022)
- [CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency](https://mlsanity.com/paper/2302.08681/) (2023)
- [A Practical Guide to Robust Optimization](https://mlsanity.com/paper/1501.02634/) (2015)
- [Competitive Algorithms for the Online Multiple Knapsack Problem with Application to Electric Vehicle Charging](https://mlsanity.com/paper/2010.00412/) (2020)
- [Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud](https://mlsanity.com/paper/2110.13234/) (2021)
- [Mixing predictions for online metric algorithms](https://mlsanity.com/paper/2304.01781/) (2023)
- [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)
- [GreenScale: Carbon-Aware Systems for Edge Computing](https://mlsanity.com/paper/2304.00404/) (2023)
- [Online Algorithms with Uncertainty-Quantified Predictions](https://mlsanity.com/paper/2310.11558/) (2023)
- [On the Limitations of Carbon-Aware Temporal and Spatial Workload Shifting in the Cloud](https://mlsanity.com/paper/2306.06502/) (2023)
- [Algorithms with Calibrated Machine Learning Predictions](https://mlsanity.com/paper/2502.02861/) (2025)
- [Near-Optimal Consistency-Robustness Trade-Offs for Learning-Augmented Online Knapsack Problems](https://mlsanity.com/paper/2406.18752/) (2024)
- [EnsembleCI: Ensemble Learning for Carbon Intensity Forecasting](https://mlsanity.com/paper/2505.01959/) (2025)
- [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)
- [Learning-Augmented Decentralized Online Convex Optimization in Networks](https://mlsanity.com/paper/2306.10158/) (2023)
- [Carbon-Aware Quantification of Real-Time Aggregate Power Flexibility of Electric Vehicles](https://mlsanity.com/paper/2409.05597/) (2024)

## Abstract (arXiv)

A wide range of sustainability and grid-integration strategies depend on workload shifting, which aligns the timing of energy consumption with external signals such as grid curtailment events, carbon intensity, or time-of-use electricity prices. The main challenge lies in the online nature of the problem: operators must make real-time decisions (e.g., whether to consume energy now) without knowledge of the future. While forecasts of signal values are typically available, prior work on learning-augmented online algorithms has relied almost exclusively on simple point forecasts. In parallel, the forecasting research has made significant progress in uncertainty quantification (UQ), which provides richer and more fine-grained predictive information. In this paper, we study how online workload shifting can leverage UQ predictors to improve decision-making. We introduce textttUQ-Advice, a learning-augmented algorithm that systematically integrates UQ forecasts through a textitdecision uncertainty score that measures how forecast uncertainty affects optimal future decisions. By introducing textitUQ-robustness, a new metric that characterizes how performance degrades with forecast uncertainty, we establish theoretical performance guarantees for textttUQ-Advice. Finally, using trace-driven experiments on carbon intensity and electricity price data, we demonstrate that textttUQ-Advice consistently outperforms robust baselines and existing learning-augmented methods that ignore uncertainty.