A Simpler Analysis for $\varepsilon$-Clairvoyant Flow Time Scheduling

2026-03-18

AI Summary

This work addresses the problem of minimizing total flow time in the $\varepsilon$-clairvoyant scheduling model, a fundamental challenge in online scheduling under partial information. The paper proposes a streamlined theoretical framework that reproves the optimality of the Shortest Lower-Bound First (SLF) algorithm. By leveraging refined competitive analysis and scheduling-theoretic tools, the authors significantly reduce the complexity of the original proof while strengthening the theoretical foundation of SLF within this model. The analysis not only offers a clearer and more rigorous argument for SLF’s optimality but also highlights its robustness and efficiency in scheduling scenarios with incomplete information about job characteristics.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Other Foundations of Search & Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

We simplify the proof of the optimality of the Shortest Lower-Bound First (SLF) algorithm, introduced by Gupta, Kaplan, Lindermayr, Schlöter, and Yingchareonthawornchai [FOCS'25], for minimizing the total flow time in the $\varepsilon$-clairvoyant setting.

Problem

• flow time
• scheduling
• ε-clairvoyant
• optimality

Innovation

• ε-clairvoyant scheduling
• flow time minimization
• Shortest Lower-Bound First
• algorithmic analysis
• simplified proof