Contract Scheduling With Predictions
Authors: Spyros Angelopoulos, Shahin Kamali11726-11733
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present the experimental evaluation of our schedules. We use exponential schedules (without any prediction) as the baseline for our comparisons. We report results for r = 4, but we note that for r > 4 the experiments show the same trends. |
| Researcher Affiliation | Academia | Spyros Angelopoulos,1,2 Shahin Kamali3 1 Centre National de la Recherche Scientifique (CNRS) 2 Sorbonne Universit e, Laboratoire d informatique de Paris 6, LIP6, Paris, France 3 University of Manitoba, Winnipeg, Manitoba, Canada |
| Pseudocode | No | The paper describes algorithms but does not include any explicit pseudocode blocks or sections labeled 'Algorithm'. |
| Open Source Code | No | The paper does not include any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The experiments use synthetic data and simulated scenarios based on random variable generation (e.g., 'random (truncated) normal variable', '1,000 evenly spaced values', '1,000 random values of τ', 'η is chosen uniformly at random'). There is no mention of a publicly available dataset with concrete access information (link, DOI, citation). |
| Dataset Splits | No | The paper does not specify training, validation, and test dataset splits. The experiments are simulation-based and evaluate the proposed schedules under various interruption time and prediction error scenarios. |
| Hardware Specification | No | The paper does not provide any specific hardware details (such as GPU/CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We fix the number n of queries to be equal to 100, and as in the previous setting, we also set H = 0.1. ... We run the experiment over 1,000 evenly spaced values of the interruption time in the interval [2, 220]. For each value of T [2, 220], we compute the acceleration ratio of the schedule for 1,000 random values of τ [T H, T + H], and report the average. |