Scheduling with Untrusted Predictions
Authors: Evripidis Bampis, Konstantinos Dogeas, Alexander Kononov, Giorgio Lucarelli, Fanny Pascual
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, we confirm the above theoretical bounds by conducting experimental evaluation comparing the proposed algorithms to the offline optimal ones for both the single and multiple machines settings. |
| Researcher Affiliation | Academia | 1Sorbonne Universit e, CNRS, LIP6, F-75005 Paris, France 2Novosibirsk State University, Novosibirsk, Russia 3Sobolev Institute of Mathematics, Novosibirsk, Russia 4Universit e de Lorraine, LCOMS, Metz, France |
| Pseudocode | No | The paper describes algorithms such as SRPPT and SPPT(m) in prose, accompanied by lemmas and theorems, but does not present them in a structured pseudocode block or a clearly labeled 'Algorithm' section with step-by-step instructions. |
| Open Source Code | Yes | The code is publicly available at: https://github.com/ildoge/SUP. |
| Open Datasets | No | We create artificial instances, each one consisting of n = 50 jobs for the single machine case, following the same approach as in [Purohit et al., 2018]. We draw the actual processing time values, xj, independently for each job from a Pareto distribution with a parameter α = 1.1. |
| Dataset Splits | No | The paper describes generating artificial instances and performing independent runs, but it does not specify explicit training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as CPU/GPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions implementing algorithms but does not specify any software dependencies or their version numbers (e.g., specific programming languages, libraries, or solvers with versions). |
| Experiment Setup | Yes | We use values of σ that belong in [0, 5000] using a step of 50. The performance of the algorithms is compared to the optimal Shortest Remaining Processing Time First (SRPT) algorithm using the actual processing values. We set τ = 0.1 for the first simulations. ... with λ = 1/2, which performs well for small values of error while remaining competitive to Round Robin when the error is big. |