{"ID":2862929,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26511","arxiv_id":"2509.26511","title":"Signal-Aware Workload Shifting Algorithms with Uncertainty-Quantified Predictors","abstract":"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 $\\texttt{UQ-Advice}$, a learning-augmented algorithm that systematically integrates UQ forecasts through a $\\textit{decision uncertainty score}$ that measures how forecast uncertainty affects optimal future decisions. By introducing $\\textit{UQ-robustness}$, a new metric that characterizes how performance degrades with forecast uncertainty, we establish theoretical performance guarantees for $\\texttt{UQ-Advice}$. Finally, using trace-driven experiments on carbon intensity and electricity price data, we demonstrate that $\\texttt{UQ-Advice}$ consistently outperforms robust baselines and existing learning-augmented methods that ignore uncertainty.","short_abstract":"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 mus...","url_abs":"https://arxiv.org/abs/2509.26511","url_pdf":"https://arxiv.org/pdf/2509.26511v1","authors":"[\"Ezra Johnson\",\"Adam Lechowicz\",\"Mohammad Hajiesmaili\"]","published":"2025-09-30T16:51:35Z","proceeding":"cs.DS","tasks":"[\"cs.DS\",\"cs.LG\"]","methods":"[]","has_code":false}