Data for Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness ...

• Main Authors: Giacomoni, Luca, Parisis, George
• Format: Dataset
• Language: unknown
• Published: University of Sussex 2024
• Subjects: Orca
• Online Access: https://dx.doi.org/10.25377/sussex.24970173.v1; https://sussex.figshare.com/articles/dataset/Data_for_Reinforcement_Learning-based_Congestion_Control_A_Systematic_Evaluation_of_Fairness_Efficiency_and_Responsiveness/24970173/1

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 ...