On Preemption and Learning in Stochastic Scheduling
Authors: Nadav Merlis, Hugo Richard, Flore Sentenac, Corentin Odic, Mathieu Molina, Vianney Perchet
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Collaboration | 1 ENSAE, Paris 2 Criteo, Paris 3 Inria, France. |
| Pseudocode | Yes | Algorithm 1 Non-Preemptive Algorithms routine; Algorithm 2 Preemptive Algorithms routine; Algorithm 3 Explore-Then-Commit Uniform (ETC-U); Algorithm 4 Upper-Confidence-Bound-Uniform (UCB-U); Algorithm 5 Explore-Then-Commit-Round-Robin (ETC-RR); Algorithm 6 Upper-Confidence-Bound-Round-Robin (UCB-RR) |
| Open Source Code | Yes | The code to reproduce experiemnts is available at https: //github.com/hugorichard/ml4a-scheduling. |
| Open Datasets | No | The paper uses synthetic experiments with jobs grouped by types, rather than a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes synthetic experiments with varying parameters (e.g., number of jobs n, λ values) but does not define or use traditional train/validation/test dataset splits. |
| Hardware Specification | Yes | Computations are run on a laptop. |
| Software Dependencies | No | All code is written in Python. We use matplotlib (Hunter, 2007) for plotting, and numpy (Harris et al., 2020) for array manipulations. The paper mentions software names and cites them, but does not provide specific version numbers for these software packages or other dependencies. |
| Experiment Setup | Yes | The first experiment plots the CR of each algorithm for two types of jobs and fixed values of λ1, λ2 as n varies (see Figure 1). ... In the second experiment, n = 50 and λ2 = 1 are fixed, while λ1 varies in (0, 1) (see Figure 2). |