Knowledge-Defined Networking
Training datasets to encourage open research, development and benchmarking of Machine Learning algorithms applied to Computer Networks.
Barcelona
Pinned Repositories
-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
Knowledge-Defined Networking's Repositories
knowledgedefinednetworking/a-deep-rl-approach-for-sdn-routing-optimization
A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization
knowledgedefinednetworking/DRL-GNN
knowledgedefinednetworking/demo-routenet
Demo of RouteNet in ACM SIGCOMM'19
knowledgedefinednetworking/Unveiling-the-potential-of-GNN-for-network-modeling-and-optimization-in-SDN
knowledgedefinednetworking/net2vec
This repository is a collection of machine learning models for computer networks.
knowledgedefinednetworking/-knowledge-defined-networking
knowledgedefinednetworking/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.
knowledgedefinednetworking/network-modeling-GNN