Data for Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness ...
IntroductionThis is the dataset for the paper titled ‘Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness’ that has been accepted for publication at IEEE INFOCOM 2024. The paper’s accepted version will be available following publication...
Main Authors: | , |
---|---|
Format: | Dataset |
Language: | unknown |
Published: |
University of Sussex
2024
|
Subjects: | |
Online Access: | https://dx.doi.org/10.25377/sussex.24970173 https://sussex.figshare.com/articles/dataset/Data_for_Reinforcement_Learning-based_Congestion_Control_A_Systematic_Evaluation_of_Fairness_Efficiency_and_Responsiveness/24970173 |
id |
ftdatacite:10.25377/sussex.24970173 |
---|---|
record_format |
openpolar |
spelling |
ftdatacite:10.25377/sussex.24970173 2024-02-27T08:44:19+00:00 Data for Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness ... Giacomoni, Luca Parisis, George 2024 https://dx.doi.org/10.25377/sussex.24970173 https://sussex.figshare.com/articles/dataset/Data_for_Reinforcement_Learning-based_Congestion_Control_A_Systematic_Evaluation_of_Fairness_Efficiency_and_Responsiveness/24970173 unknown University of Sussex https://dx.doi.org/10.25377/sussex.24978162 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 dataset Dataset 2024 ftdatacite https://doi.org/10.25377/sussex.2497017310.25377/sussex.24978162 2024-02-01T16:35:19Z IntroductionThis is the dataset for the paper titled ‘Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness’ that has been accepted for publication at IEEE INFOCOM 2024. The paper’s accepted version will be available following publication in May 2024 at https://sussex.figshare.com/articles/conference_contribution/Reinforcement_learningbased_congestion_control_a_systematic_evaluation_of_fairness_efficiency_and_responsiveness/24711033. The dataset is meant to be used in conjunction with the codebase that is also made available at https://doi.org/10.25377/sussex.24978162.However, the dataset itself is of value to researchers as it contains an extensive set of metrics captured during experimentation with Reinforcement Learning-based Congestion control as discussed in the ‘Experimental Evaluation’ section of the paper. Our study is the result of a 160-hour long experimentation during which 1950 Orca, Aurora and TCP Cubic flows were measured. We have ... Dataset Orca DataCite Metadata Store (German National Library of Science and Technology) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
description |
IntroductionThis is the dataset for the paper titled ‘Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness’ that has been accepted for publication at IEEE INFOCOM 2024. The paper’s accepted version will be available following publication in May 2024 at https://sussex.figshare.com/articles/conference_contribution/Reinforcement_learningbased_congestion_control_a_systematic_evaluation_of_fairness_efficiency_and_responsiveness/24711033. The dataset is meant to be used in conjunction with the codebase that is also made available at https://doi.org/10.25377/sussex.24978162.However, the dataset itself is of value to researchers as it contains an extensive set of metrics captured during experimentation with Reinforcement Learning-based Congestion control as discussed in the ‘Experimental Evaluation’ section of the paper. Our study is the result of a 160-hour long experimentation during which 1950 Orca, Aurora and TCP Cubic flows were measured. We have ... |
format |
Dataset |
author |
Giacomoni, Luca Parisis, George |
spellingShingle |
Giacomoni, Luca Parisis, George Data for Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness ... |
author_facet |
Giacomoni, Luca Parisis, George |
author_sort |
Giacomoni, Luca |
title |
Data for Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness ... |
title_short |
Data for Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness ... |
title_full |
Data for Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness ... |
title_fullStr |
Data for Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness ... |
title_full_unstemmed |
Data for Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness ... |
title_sort |
data for reinforcement learning-based congestion control: a systematic evaluation of fairness, efficiency and responsiveness ... |
publisher |
University of Sussex |
publishDate |
2024 |
url |
https://dx.doi.org/10.25377/sussex.24970173 https://sussex.figshare.com/articles/dataset/Data_for_Reinforcement_Learning-based_Congestion_Control_A_Systematic_Evaluation_of_Fairness_Efficiency_and_Responsiveness/24970173 |
genre |
Orca |
genre_facet |
Orca |
op_relation |
https://dx.doi.org/10.25377/sussex.24978162 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.25377/sussex.2497017310.25377/sussex.24978162 |
_version_ |
1792052713855385600 |