RouteNet-Fermi: Network Modeling with Graph Neural Networks

• URL: http://arxiv.org/abs/2212.12070v3
• Date: Wed, 20 Sep 2023 07:42:10 GMT
• Title: RouteNet-Fermi: Network Modeling with Graph Neural Networks
• Authors: Miquel Ferriol-Galm\'es, Jordi Paillisse, Jos\'e Su\'arez-Varela, Krzysztof Rusek, Shihan Xiao, Xiang Shi, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio
• Abstract summary: We present RouteNet-Fermi, a custom Graph Neural Networks (GNN) model that shares the same goals as Queuing Theory. The proposed model predicts accurately the delay, jitter, and packet loss of a network. Our experimental results show that RouteNet-Fermi achieves similar accuracy as computationally-expensive packet-level simulators.
• Score: 7.227467283378366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Network models are an essential block of modern networks. For example, they are widely used in network planning and optimization. However, as networks increase in scale and complexity, some models present limitations, such as the assumption of Markovian traffic in queuing theory models, or the high computational cost of network simulators. Recent advances in machine learning, such as Graph Neural Networks (GNN), are enabling a new generation of network models that are data-driven and can learn complex non-linear behaviors. In this paper, we present RouteNet-Fermi, a custom GNN model that shares the same goals as Queuing Theory, while being considerably more accurate in the presence of realistic traffic models. The proposed model predicts accurately the delay, jitter, and packet loss of a network. We have tested RouteNet-Fermi in networks of increasing size (up to 300 nodes), including samples with mixed traffic profiles -- e.g., with complex non-Markovian models -- and arbitrary routing and queue scheduling configurations. Our experimental results show that RouteNet-Fermi achieves similar accuracy as computationally-expensive packet-level simulators and scales accurately to larger networks. Our model produces delay estimates with a mean relative error of 6.24% when applied to a test dataset of 1,000 samples, including network topologies one order of magnitude larger than those seen during training. Finally, we have also evaluated RouteNet-Fermi with measurements from a physical testbed and packet traces from a real-life network.

Related papers

• Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch [72.26822499434446]
  Auto-Train-Once (ATO) is an innovative network pruning algorithm designed to automatically reduce the computational and storage costs of DNNs. We provide a comprehensive convergence analysis as well as extensive experiments, and the results show that our approach achieves state-of-the-art performance across various model architectures.
  arXiv  Detail & Related papers  (2024-03-21T02:33:37Z)
• Building a Graph-based Deep Learning network model from captured traffic traces [4.671648049111933]
  State of the art network models are based or depend on Discrete Event Simulation (DES) DES is highly accurate, it is also computationally costly and cumbersome to parallelize, making it unpractical to simulate high performance networks. We propose a Graph Neural Network (GNN)-based solution specifically designed to better capture the complexities of real network scenarios.
  arXiv  Detail & Related papers  (2023-10-18T11:16:32Z)
• Open World Learning Graph Convolution for Latency Estimation in Routing Networks [16.228327606985257]
  We propose a novel approach for modeling network routing, using Graph Neural Networks. Our model shares a stable performance across different network sizes and configurations of routing networks, while at the same time being able to extrapolate towards unseen sizes, configurations, and user behavior. We show that our model outperforms most conventional deep-learning-based models, in terms of prediction accuracy, computational resources, inference speed, as well as ability to generalize towards open-world input.
  arXiv  Detail & Related papers  (2022-07-08T19:26:40Z)
• RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation [5.56275556529722]
  We present emphRouteNet-Erlang, a pioneering GNN architecture designed to model computer networks. RouteNet-Erlang supports complex traffic models, multi-queue scheduling policies, routing policies and can provide accurate estimates. We benchmark RouteNet-Erlang against a state-of-the-art QT model, and our results show that it outperforms QT in all the network scenarios.
  arXiv  Detail & Related papers  (2022-02-28T17:09:53Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network [0.0]
  Edge nodes are crucial for detection against multitudes of cyber attacks on Internet-of-Things endpoints. We develop a novel light, fast and accurate 'Edge-Detect' model, which detects Denial of Service attack on edge nodes using DLM techniques.
  arXiv  Detail & Related papers  (2021-02-03T04:24:34Z)
• Applying Graph-based Deep Learning To Realistic Network Scenarios [5.453745629140304]
  This paper presents a new Graph-based deep learning model able to estimate accurately the per-path mean delay in networks. The proposed model can generalize successfully over topologies, routing configurations, queue scheduling policies and traffic matrices unseen during the training phase.
  arXiv  Detail & Related papers  (2020-10-13T20:58:59Z)
• Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks [78.65792427542672]
  Dynamic Graph Network (DG-Net) is a complete directed acyclic graph, where the nodes represent convolutional blocks and the edges represent connection paths. Instead of using the same path of the network, DG-Net aggregates features dynamically in each node, which allows the network to have more representation ability.
  arXiv  Detail & Related papers  (2020-10-02T16:50:26Z)
• Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio [101.84651388520584]
  This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs. Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-06T15:51:00Z)
• Analyzing Neural Networks Based on Random Graphs [77.34726150561087]
  We perform a massive evaluation of neural networks with architectures corresponding to random graphs of various types. We find that none of the classical numerical graph invariants by itself allows to single out the best networks. We also find that networks with primarily short-range connections perform better than networks which allow for many long-range connections.
  arXiv  Detail & Related papers  (2020-02-19T11:04:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.