Non-Clairvoyant Scheduling with Progress Bars

Ziyad Benomar, Romain Cosson, Alexander Lindermayr, Jens Schlöter

Keywords: online algorithms, competitive analysis, scheduling, algorithms with predictions, non-clairvoyance

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.

Primary Area: Theory (e.g., control theory, learning theory, algorithmic game theory)

Submission Number: 1979