Deep Reinforcement Learning for Topology-Aware VNF Resource Prediction in NFV Environments

Nikita Jalodia, Shagufta Henna, Alan Davy

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

12 Citations (Scopus)

Abstract

Network Function Virtualisation (NFV) has emerged as a key paradigm in network softwarisation, enabling virtualisation in future generation networks. Once deployed, the Virtual Network Functions (VNFs) in an NFV application's Service Function Chain (SFC) experience dynamic fluctuations in network traffic and requests, which necessitates dynamic scaling of resource instances. Dynamic resource management is a critical challenge in virtualised environments, specifically while balancing the trade-off between efficiency and reliability. Since provisioning of virtual infrastructures is time-consuming, this negates the Quality of Service (QoS) requirements and reliability criterion in latency-critical applications such as autonomous driving. This calls for predictive scaling decisions to balance the provisioning time sink, with a methodology that preserves the topological dependencies between the nodes in an SFC for effective resource forecasting. To address this, we propose the model for an Asynchronous Deep Reinforcement Learning (DRL) enhanced Graph Neural Networks (GNN) for topology-aware VNF resource prediction in dynamic NFV environments.

Original languageEnglish
Title of host publicationIEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2019 - Proceedings
EditorsLarry Horner, Kurt Tutschku, Fabrizio Granelli, Yuji Sekiya, Marco Tacca, Deval Bhamare, Helge Parzyjegla
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728145457
DOIs
Publication statusPublished - Nov 2019
Event2019 IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2019 - Dallas, United States
Duration: 12 Nov 201914 Nov 2019

Publication series

NameIEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2019 - Proceedings

Conference

Conference2019 IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2019
Country/TerritoryUnited States
CityDallas
Period12/11/201914/11/2019

Keywords

  • Asynchronous Deep Q-Learning
  • Deep Learning
  • Deep Reinforcement Learning
  • Dynamic Resource Prediction
  • Future Generation Networks
  • Graph Neural Networks
  • Machine Learning
  • NFV
  • Prediction
  • Topology Awareness

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