Non-clairvoyant Scheduling with Partial Predictions
Authors: Ziyad Benomar, Vianney Perchet
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we validate our theoretical findings by testing the algorithms we presented on various benchmark job sizes. In all the figures, each point is averaged over 104 independent trials. Figure 2 exhibits the empirical ratios achieved by both algorithms with a number n {20, 1000} of jobs. |
| Researcher Affiliation | Collaboration | 1ENSAE, FAIRPLAY joint team, CREST, Palaiseau, France 2Ecole polytechnique, Palaiseau, France 3Criteo AI Lab, FAIRPLAY joint team, Paris, France. |
| Pseudocode | Yes | Algorithm 1 Catch-up and Resume Round-Robin (CRRR); Algorithm 2 Switch algorithm Switch(zσ, x); Algorithm 3 imperfect predictions Switch(ξyσ, x) |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes generating synthetic data using specific distributions (e.g., 'i.i.d. job sizes sampled from the exponential distribution with parameter 1', 'job sizes drawn from the distribution Φ(r, a)', 'a synthetic instance of n = 50 job sizes, drawn independently from the Pareto distribution'), rather than referring to a publicly available dataset with concrete access information (link, DOI, or formal citation). |
| Dataset Splits | No | The paper describes the generation of job sizes for experiments but does not provide specific details on how these generated instances are split into training, validation, or test sets, nor does it refer to predefined splits from established benchmarks. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory specifications, or cloud instance types). |
| Software Dependencies | No | The paper does not list specific versions of any software, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | We generate a synthetic instance of n = 50 job sizes, drawn independently from the Pareto distribution with scale 1 and shape 1.1. Furthermore, we consider noisy predictions yi = xi + εi for all i [50], where εi is sampled independently from a normal distribution with mean 0 and standard deviation τ. The left plot displays the ratios for different λ and ρ values, with B = 25 = n/2. |