Permutation Predictions for Non-Clairvoyant Scheduling
This addresses the problem of online scheduling with unknown job processing times for researchers and practitioners in algorithm design, offering a novel prediction approach that is incremental but extends to more complex settings.
The paper tackles non-clairvoyant scheduling by introducing a new prediction model that provides a relative order of jobs, showing that these predictions are learnable and lead to algorithms with strong performance guarantees, including the first learning-augmented results for weighted jobs and unrelated machines, with empirical experiments demonstrating superior performance compared to previous single-machine algorithms.
In non-clairvoyant scheduling, the task is to find an online strategy for scheduling jobs with a priori unknown processing requirements with the objective to minimize the total (weighted) completion time. We revisit this well-studied problem in a recently popular learning-augmented setting that integrates (untrusted) predictions in online algorithm design. While previous works used predictions on processing requirements, we propose a new prediction model, which provides a relative order of jobs which could be seen as predicting algorithmic actions rather than parts of the unknown input. We show that these predictions have desired properties, admit a natural error measure as well as algorithms with strong performance guarantees and that they are learnable in both, theory and practice. We generalize the algorithmic framework proposed in the seminal paper by Kumar et al. (NeurIPS'18) and present the first learning-augmented scheduling results for weighted jobs and unrelated machines. We demonstrate in empirical experiments the practicability and superior performance compared to the previously suggested single-machine algorithms.