A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization
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Updated
Oct 17, 2022 - Python
A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization
A platform to test reinforcement learning policies in the datacenter setting.
YATES (Yet Another Traffic Engineering System)
Joint placement and scaling of bidirectional network services with stateful virtual or physical network functions
In-band Network Telemetry(INT) using P4 for traffic engineering
Microsoft's open source max-min fair solver for cluster scheduling and traffic engineering
[SIGCOMM 2021] ARROW: Restoration-Aware Traffic Engineering
Robust decomposition, anomaly and change point detection methods for use on automobile traffic data.
A framework for analysis and modeling of IP network flows. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021001680
Infrastructure as Code & Software Defined Networking Hackathons
Python parser for the REPETITA data format
Maximising throughput in cross traffic flows in a L2/L3 network. Contains code and poster for AdvNet at ETH Zurich, HS 2020.
Research on the policy and current situation of "Giving way to pedestrians" in Beijing. 北京 "车让人" 的政策和实地现状调研. 《交通工程》大作业
Traffic Engineering Lab, Dept. of Urban Planning & Engineering, Yonsei University
Computational Transportation Science (CTS) resources for everyone!
[NSDI 2021] A Social Network Under Social Distancing: Risk-Driven Backbone Management During COVID-19 and Beyond
Intelligent Routing and Bandwidth Allocation System with Reinformance Learning(IRBRL)
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