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-knowledge-defined-networking

a-deep-rl-approach-for-sdn-routing-optimization

A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization

demo-routenet

Demo of RouteNet in ACM SIGCOMM'19

drl-gnn

net2vec

This repository is a collection of machine learning models for computer networks.

network-modeling-gnn

understanding-the-modeling-of-network-delays-using-nn

Recent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. In this context, ML can be used as a computer network modeling technique to build models that estimate the network performance. Indeed, network modeling is a central technique to many networking functions, for instance in the field of optimization, in which the model is used to search a configuration that satisfies the target policy. In this paper, we aim to provide an answer to the following question: Can neural networks accurately model the delay of a computer network as a function of the input traffic? For this, we assume the network as a black-box that has as input a traffic matrix and as output delays. Then we train different neural networks models and evaluate its accuracy under different fundamental network characteristics: topology, size, traffic intensity and routing. With this, we aim to have a better understanding of computer network modeling with neural nets and ultimately provide practical guidelines on how such models need to be trained.

unveiling-the-potential-of-gnn-for-network-modeling-and-optimization-in-sdn