Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques do not meet the requirements to provide accurate estimations of relevant performance metrics such as delay and jitter. In this paper we propose a novel Graph Neural Network (GNN) model able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination pair mean delay and jitter. GNN are tailored to learn and model information structured as graphs and as a result, our model is able to generalize over arbitrary topologies, routing schemes and vari...
Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle comp...
Today, network operators still lack functional network models able to make accurate predictions of e...
Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle comp...
Network modeling is a critical component for building self-driving Software-Defined Networks, partic...
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
Today, network operators still lack functional network models able to make accurate predictions of e...
Autonomous network management is crucial for Fifth Generation (5G) and Beyond 5G (B5G) networks, whe...
The final publication is available at ACM via http://dx.doi.org/10.1145/3229607.3229613Recent trends...
Network modeling is a critical component for building self-driving Software-Defined Networks.Traditi...
Network modeling is a critical component of Quality of Service (QoS) optimization. Current networks ...
Extreme connectivity, dynamic resource provision-ing and demand of quality assurance in 5G and Beyon...
Recently, a Graph Neural Network (GNN) model called RouteNet was proposed as a method to estimate e...
Network modeling is a fundamental tool in network research, design, and operation. Arguably the most...
Accurate routing network status estimation is a key component in Software Defined Networking. Howeve...
Autonomous Fifth Generation (5G) and Beyond 5G (B5G) networks require modelling tools to predict the...
Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle comp...
Today, network operators still lack functional network models able to make accurate predictions of e...
Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle comp...
Network modeling is a critical component for building self-driving Software-Defined Networks, partic...
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
Today, network operators still lack functional network models able to make accurate predictions of e...
Autonomous network management is crucial for Fifth Generation (5G) and Beyond 5G (B5G) networks, whe...
The final publication is available at ACM via http://dx.doi.org/10.1145/3229607.3229613Recent trends...
Network modeling is a critical component for building self-driving Software-Defined Networks.Traditi...
Network modeling is a critical component of Quality of Service (QoS) optimization. Current networks ...
Extreme connectivity, dynamic resource provision-ing and demand of quality assurance in 5G and Beyon...
Recently, a Graph Neural Network (GNN) model called RouteNet was proposed as a method to estimate e...
Network modeling is a fundamental tool in network research, design, and operation. Arguably the most...
Accurate routing network status estimation is a key component in Software Defined Networking. Howeve...
Autonomous Fifth Generation (5G) and Beyond 5G (B5G) networks require modelling tools to predict the...
Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle comp...
Today, network operators still lack functional network models able to make accurate predictions of e...
Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle comp...