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
🛜 Create and manage virtual networks through simple YAML configuration files
Microsoft's open source max-min fair solver for cluster scheduling and traffic engineering
[SIGCOMM 2021] ARROW: Restoration-Aware Traffic Engineering
Controller Leveraging Intent-Based Networking and SRv6 for Dynamic End to End Traffic Steering
Intelligent Routing and Bandwidth Allocation System with Reinformance Learning(IRBRL)
Computational Transportation Science (CTS) resources for everyone!
Robust decomposition, anomaly and change point detection methods for use on automobile traffic data.
Infrastructure as Code & Software Defined Networking Hackathons
[NSDI 2021] A Social Network Under Social Distancing: Risk-Driven Backbone Management During COVID-19 and Beyond
Implementing self-healing autonomic software-defined Industrial IoT-Edge communication networks in offshore wind power plants
A framework for analysis and modeling of IP network flows. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021001680
A Python-based traffic engineering framework that jointly optimizes link weights and waypoint routing to balance Maximum Link Utilization (MLU) and end-to-end latency. Includes NSGA-II multi-objective optimization and several heuristic baselines. Supports real topologies from SNDLib and TopologyZoo.
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