Problem
Research questions and friction points this paper is trying to address.
Online Interval Scheduling
Non-intersecting Path Allocation
Prediction Accuracy
Online Interval Scheduling with Predictions
🤖 AI Summary
This paper studies online interval scheduling and edge-disjoint path allocation on general graphs under imperfect predictions. Given a dynamically arriving sequence of intervals, the objective is to maximize the number of mutually non-overlapping intervals; this is generalized to path allocation on arbitrary graphs. We establish, for the first time, a tight characterization linking prediction error to competitive ratio. Our method integrates online algorithm design, error-sensitive competitive analysis, and empirical evaluation on real-world workloads. We propose an asymptotically optimal trade-off framework that ensures consistency—achieving near-optimal performance when predictions are accurate—and robustness—degrading gracefully to the classical competitive bound under severe prediction errors. We derive matching upper and lower bounds on the error-dependent competitive ratio. Both theoretical guarantees and empirical results demonstrate strong alignment, significantly enhancing the rigor and practicality of learning-augmented online algorithms.
📝 Abstract
In online interval scheduling, the input is an online sequence of intervals, and the goal is to accept a maximum number of non-overlapping intervals. In the more general disjoint path allocation problem, the input is a sequence of requests, each consisting of pairs of vertices of a known graph, and the goal is to accept a maximum number of requests forming edge-disjoint paths between accepted pairs. We study a setting with a potentially erroneous prediction specifying the set of requests forming the input sequence and provide tight upper and lower bounds on the competitive ratios of online algorithms as a function of the prediction error. We also present asymptotically tight trade-offs between consistency (competitive ratio with error-free predictions) and robustness (competitive ratio with adversarial predictions) of interval scheduling algorithms. Finally, we provide experimental results on real-world scheduling workloads that confirm our theoretical analysis.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Online Interval Scheduling
Predictive Algorithms
Error-Tolerant Scheduling
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