Fair Scheduling for Time-dependent Resources
Authors: Bo Li, Minming Li, Ruilong Zhang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate the performance of Matching-Bag Filling on randomly generated data sets, and compare it with the extensively adopted heuristic algorithm Round-Robin. In our experiment, as shown in Figure 1, we randomly generate a set of rigid jobs J (|J| = 100, 500, 1000) and a set of agents A following some distribution... |
| Researcher Affiliation | Academia | Bo Li Department of Computing The Hong Kong Polytechnic University Hung Hom, Hong Kong comp-bo.li@polyu.edu.hk Minming Li Department of Computer Science City University of Hong Kong Kowloon Tang, Hong Kong minming.li@cityu.edu.hk Ruilong Zhang Department of Computer Science City University of Hong Kong Kowloon Tang, Hong Kong ruilzhang4-c@my.cityu.edu.hk |
| Pseudocode | Yes | Algorithm 1. Matching Procedure, Algorithm 2. Bag Filling Procedure, Algorithm 3. Main Algorithm: Matching-Bag Filling, Algorithm 4. Efficient Implementation: Matching-Bag Filling, Algorithm 5. m-Matching + Inner-Greedy. |
| Open Source Code | No | The paper does not include any explicit statement about releasing its source code, nor does it provide a link to a code repository for the described methodology. |
| Open Datasets | No | In our experiment, as shown in Figure 1, we randomly generate a set of rigid jobs J (|J| = 100, 500, 1000) and a set of agents A following some distribution, where for i = 1, 2, 3, U/P/N.i means there are |A| = 5 i agents whose values are randomly generated from a(n) Uniform, Poisson, or Normal distribution. The paper generates its own data and does not provide public access information (link, citation, repository) for the generated datasets. |
| Dataset Splits | No | In our experiment, as shown in Figure 1, we randomly generate a set of rigid jobs J (|J| = 100, 500, 1000) and a set of agents A following some distribution... For each setting, we generate 1000 instances. The paper generates instances for evaluation but does not specify train/validation/test splits as it's not using a pre-existing dataset that typically has such splits. |
| Hardware Specification | No | The paper mentions running experiments but does not provide specific hardware details (e.g., GPU/CPU models, memory) used for the computations. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions like Python 3.8, CPLEX 12.4) that would be needed to replicate the experiments. |
| Experiment Setup | Yes | In our experiment, as shown in Figure 1, we randomly generate a set of rigid jobs J (|J| = 100, 500, 1000) and a set of agents A following some distribution, where for i = 1, 2, 3, U/P/N.i means there are |A| = 5 i agents whose values are randomly generated from a(n) Uniform, Poisson, or Normal distribution. For each setting, we generate 1000 instances. |