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

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

Introduction This 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 collected approximately 500GB of data encompassing diverse metrics related to network interfaces (e.g., utilisation, retransmissions, packet drops), CPU and memory parameters (such as CPU load and memory usage), as well as the data transport layer (e.g., congestion window, round trip time). Reinforcement learning (RL)-based congestion control (CC) promises efficient CC in a fast-changing networking landscape, where evolving communication technologies, applications and traffic workloads pose severe challenges to human-derived, static CC algorithms. RL-based CC is in its early days and substantial research is required to understand existing limitations, identify research challenges and, eventually, yield deployable solutions for real-world networks. In this paper we present the first reproducible and systematic study of RL-based CC with the aim to highlight strengths and uncover fundamental limitations of the state-of-the-art. We identify challenges in evaluating RL-based CC, establish a methodology for ...