Dominant Resource Fairness with Meta-Types

Authors: Steven Yin, Shatian Wang, Lingyi Zhang, Christian Kroer

IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Finally, we show numerically that our method scales better to large problems than alternative approaches.5 Numerical Experiments We compare the algorithms on running time, normalized max envy, and social welfare.
Researcher Affiliation Academia Steven Yin , Shatian Wang , Lingyi Zhang and Christian Kroer IEOR Department, Columbia University {sy2737, sw3219, lz2573, christian.kroer}@columbia.edu
Pseudocode Yes Algorithm 1: Dominant Resource Fairness with Meta Types (DRF-MT)
Open Source Code No The paper mentions using Gurobi and Mosek for implementation but does not provide any link or statement indicating that the code for their proposed method is open-source or publicly available.
Open Datasets No We fix a meta-type structure (Ω1 = {0}, Ω2 = {1, 2}, Ω3 = {3, 4, 5}, Ω4 = {6, 7, 8, 9}) and randomly generate the demands, group structures, and weights for the agents.
Dataset Splits No The paper states that data was randomly generated but does not specify any training, validation, or test splits. It does not provide sufficient details for data partitioning.
Hardware Specification No The paper mentions general concepts like "compute server" or "compute cluster" but does not specify any particular hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments.
Software Dependencies Yes Gurobi [2021] and Mosek (Aps [2020]) are used to implement the algorithms.
Experiment Setup Yes We fix a meta-type structure (Ω1 = {0}, Ω2 = {1, 2}, Ω3 = {3, 4, 5}, Ω4 = {6, 7, 8, 9}) and randomly generate the demands, group structures, and weights for the agents. For each choice of number of agents, we ran 16 trials. More details on the experimental setup and additional experiments can be found in Appendix B .