# Online Interval Scheduling with Predictions

## TL;DR

This paper investigates online interval scheduling and disjoint path allocation using predictions, establishing bounds on algorithm performance based on prediction accuracy and validating findings with real-world experiments.

## Contribution

It introduces a framework analyzing the impact of prediction errors on online scheduling algorithms, providing tight bounds and trade-offs between consistency and robustness.

## Key findings

- Tight bounds on competitive ratios as a function of prediction error.
- Asymptotically optimal trade-offs between consistency and robustness.
- Experimental validation on real-world workloads confirming theoretical results.

## 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.