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Poster
On Preemption and Learning in Stochastic Scheduling
Nadav Merlis · Hugo Richard · Flore Sentenac · Corentin Odic · Mathieu Molina · Vianney Perchet

Tue Jul 25 05:00 PM -- 06:30 PM (PDT) @ Exhibit Hall 1 #814
in Poster Session 2 »

We study single-machine scheduling of jobs, each belonging to a job type that determines its duration distribution. We start by analyzing the scenario where the type characteristics are known and then move to two learning scenarios where the types are unknown: non-preemptive problems, where each started job must be completed before moving to another job; and preemptive problems, where job execution can be paused in the favor of moving to a different job. In both cases, we design algorithms that achieve sublinear excess cost, compared to the performance with known types, and prove lower bounds for the non-preemptive case. Notably, we demonstrate, both theoretically and through simulations, how preemptive algorithms can greatly outperform non-preemptive ones when the durations of different job types are far from one another, a phenomenon that does not occur when the type durations are known.

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Author Information

Nadav Merlis (ENSAE Paris)
Hugo Richard (ENSAE)
Flore Sentenac (CREST)
Corentin Odic
Mathieu Molina (INRIA)
Vianney Perchet (ENS Paris-Saclay & Criteo AI Lab)

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