Non-Clairvoyant Scheduling with Progress Bars

2025-09-23

AI Summary

This paper studies non-clairvoyant scheduling to minimize total completion time using only progress-bar feedback—not exact processing times. We address two settings: adversarial and stochastic progress estimation. To this end, we propose a novel “progress-bar–enhanced scheduling” framework. Methodologically, we design a generic algorithm composition mechanism, improve the competitive ratio guarantees of learning-augmented scheduling, and introduce a more realistic optimistic stochastic progress model. Our approach integrates online optimization, competitive analysis, stochastic modeling, and learning-augmented algorithm design. Theoretically, our framework achieves tight strong competitive ratios under adversarial progress estimates and asymptotically optimal performance under the stochastic model. Experiments demonstrate that progress-bar feedback significantly improves scheduling efficiency. Overall, this work establishes a new paradigm for real-time system scheduling in the absence of prior runtime information.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Adversarial Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In non-clairvoyant scheduling, the goal is to minimize the total job completion time without prior knowledge of individual job processing times. This classical online optimization problem has recently gained attention through the framework of learning-augmented algorithms. We introduce a natural setting in which the scheduler receives continuous feedback in the form of progress bars: estimates of the fraction of each job completed over time. We design new algorithms for both adversarial and stochastic progress bars and prove strong competitive bounds. Our results in the adversarial case surprisingly induce improved guarantees for learning-augmented scheduling with job size predictions. We also introduce a general method for combining scheduling algorithms, yielding further insights in scheduling with predictions. Finally, we propose a stochastic model of progress bars as a more optimistic alternative to conventional worst-case models, and present an asymptotically optimal scheduling algorithm in this setting.

Problem

• Minimizes job completion time without knowing processing times
• Schedules jobs using continuous progress bar feedback
• Develops algorithms for adversarial and stochastic progress bars

Innovation

• Progress bars provide continuous job completion feedback
• Algorithms designed for adversarial and stochastic progress bars
• General method combines scheduling algorithms with predictions