Predicting Latency Quantiles using Network Calculus-assisted GNNs

Max Helm, Georg Carle

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Network digital twins commonly rely on Graph Neural Networks (GNNs) as functional models. They typically predict network performance metrics, such as latencies. Most approaches have one of the following restrictions: they use simulated data, predict mean values, or don't utilize formal method results as inputs. We introduce an approach that: (I) relies on data obtained from a hardware testbed, increasing realism, (II) predicts quantiles in addition to means, increasing flexibility and applicability, (III) uses the formal method of network calculus to obtain input features, increasing prediction accuracy. We show that latencies in hardware testbeds can be predicted at different quantiles with median relative errors between 8% and 29% using a simple GNN architecture. Furthermore, we show that network calculus bounds are especially useful for predicting higher quantiles and that they mostly correct large prediction errors.

Original languageEnglish
Title of host publicationGNNet 2023 - Proceedings of the 2nd Graph Neural Networking Workshop 2023
PublisherAssociation for Computing Machinery, Inc
Pages13-18
Number of pages6
ISBN (Electronic)9798400704482
DOIs
StatePublished - 8 Dec 2023
Event2nd Graph Neural Networking Workshop, GNNet 2023 - Paris, France
Duration: 8 Dec 2023 → …

Publication series

NameGNNet 2023 - Proceedings of the 2nd Graph Neural Networking Workshop 2023

Conference

Conference2nd Graph Neural Networking Workshop, GNNet 2023
Country/TerritoryFrance
CityParis
Period8/12/23 → …

Keywords

  • delay model
  • graph neural network
  • hardware measurement
  • latency model
  • network calculus

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