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arXiv · 2302.13701

Online Interval Scheduling with Predictions

Joan Boyar·Lene M. Favrholdt·Shahin Kamali·Kim S. Larsen
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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.

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Joan Boyar, Lene M. Favrholdt, Shahin Kamali, Kim S. Larsen. 2023-02-27. Online Interval Scheduling with Predictions. https://arxiv.org/abs/2302.13701

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