Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

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 variable traffic intensity. In the paper we show that our model provides accurate estimates of delay and jitter (worst case R2 = 0.86) when testing against topologies, routing and traffic not seen during training. In addition, we present the potential of the model for network operation by presenting several use-cases that show its effective use in per-source/destination pair delay/jitter routing optimization and its generalization capabilities by reasoning in topologies and routing schemes not seen during training.This work was supported by AGH University of Science and Technology grant, under contract no. 15.11.230.400, the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE) and the Catalan Institution for Research and Advanced Studies (ICREA). The research was also supported in part by PL-Grid Infrastructure.Peer ReviewedPostprint (author's final draft

Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

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 variable traffic intensity. In the paper we show that our model provides accurate estimates of delay and jitter (worst case $R^2=0.86$) when testing against topologies, routing and traffic not seen during training. In addition, we present the potential of the model for network operation by presenting several use-cases that show its effective use in per-source/destination pair delay/jitter routing optimization and its generalization capabilities by reasoning in topologies and routing schemes not seen during training.Comment: 12 page

Towards more realistic network models based on Graph Neural Networks

Recently, a Graph Neural Network (GNN) model called RouteNet was proposed as an efficient method to estimate end-to-end network performance metrics such as delay or jitter, given the topology, routing, and traffic of the network. Despite its success in making accurate estimations and generalizing to unseen topologies, the model makes some simplifying assumptions about the network, and does not consider all the particularities of how real networks operate. In this work we extend the architecture of RouteNet to support different features on forwarding devices, specifically we focus on devices with variable queue sizes, and we experimentally evaluate the accuracy of the extended RouteNet architecture.This work was supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE), the Catalan Institution for Research and Advanced Studies (ICREA), FI-AGAUR grant by the Catalan Government and the AGH University of Science and by the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University. The research was also supported in part by PL-Grid Infrastructure.Peer ReviewedPostprint (author's final draft

Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case

Recent advances in Deep Reinforcement Learning (DRL) have shown a significant improvement in decision-making problems. The networking community has started to investigate how DRL can provide a new breed of solutions to relevant optimization problems, such as routing. However, most of the state-of-the-art DRL-based networking techniques fail to generalize, this means that they can only operate over network topologies seen during training, but not over new topologies. The reason behind this important limitation is that existing DRL networking solutions use standard neural networks (e.g., fully connected), which are unable to learn graph-structured information. In this paper we propose to use Graph Neural Networks (GNN) in combination with DRL. GNN have been recently proposed to model graphs, and our novel DRL+GNN architecture is able to learn, operate and generalize over arbitrary network topologies. To showcase its generalization capabilities, we evaluate it on an Optical Transport Network (OTN) scenario, where the agent needs to allocate traffic demands efficiently. Our results show that our DRL+GNN agent is able to achieve outstanding performance in topologies unseen during training.Comment: 11 page

Augmenting Information Propagation Models with Graph Neural Networks

Department of Computer Science and EngineeringConventional epidemic models are limited in their ability to capture the dynamics of real world epidemics in a sense that they either place restrictions on the models such as their topology and contact process for mathematical tractability, or focus only on the average global behavior, which lacks details for further analysis. We propose a novel modeling approach that augments the conventional epidemic models using Graph Neural Networks to improve their expressive power while preserving the useful mathematical structures. Simulation results show that our proposed model can predict spread times in both node-level and network-wide perspectives with high accuracy having median relative errors below 15% for a wide range of scenarios.ope

Prototipat d'una Graph Neural Network

Aquest TFG consisteix en la implementació de dos optimitzadors de "Routing Matrix" disenyats per a minimitzar la làtencia d'una xarxa mitjançant la Graph Neural Network (GNN) de RouteNet, desenvolupada per un grup de recerca de la FIB.This Final degree project consists in the implementation of two Routing Matrix optimizers designed to minimize the delay in a network by using a new Graph Neural Network (GNN), RouteNet, developed by a FIB's research group

RouteNet: leveraging graph neural networks for network modeling and optimization in SDN

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE = 15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.This work was supported in part by the Polish Ministryof Science and Higher Education with the subvention funds of the Facultyof Computer Science, Electronics and Telecommunications, AGH University,in part by the Spanish MINECO under Contract TEC2017-90034-C2-1-R(ALLIANCE), in part by the Catalan Institution for Research and AdvancedStudies (ICREA) and the FI-AGAUR Grant by the Catalan Government, andin part by PL-Grid Infrastructure.Peer ReviewedPostprint (author's final draft