On Preemption and Learning in Stochastic Scheduling
This addresses scheduling efficiency for systems with stochastic job durations, offering incremental improvements in algorithm design for unknown distributions.
The paper tackles the problem of scheduling jobs with unknown duration distributions in single-machine systems, comparing non-preemptive and preemptive approaches. It shows that preemptive algorithms achieve sublinear excess cost and can greatly outperform non-preemptive ones when job durations vary widely, as demonstrated through theoretical proofs and simulations.
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.