NPTSN: RL-Based Network Planning with Guaranteed Reliability for In-Vehicle TSSDN

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Abstract

To achieve strict reliability goals with lower redundancy cost, Time-Sensitive Software-Defined Networking (TSSDN) enables run-time recovery for future in-vehicle networks. While the recovery mechanisms rely on network planning to establish reliability guarantees, existing network planning solutions are not suitable for TSSDN due to its domain-specific scheduling and reliability concerns. The sparse solution space and expensive reliability verification further complicate the problem. We propose NPTSN, a TSSDN planning solution based on deep Reinforcement Learning (RL). It represents the domain-specific concerns with the RL environment and constructs solutions with an intelligent network generator. The network generator iteratively proposes TSSDN solutions based on a failure analysis and trains a decision-making neural network using a modified actor-critic algorithm. Extensive performance evaluations show that NPTSN guarantees reliability for more test cases and shortens the decision trajectory compared to state-of-the-art solutions. It reduces the network cost by up to 6.8x in the performed experiments.
Original languageEnglish
Title of host publicationProceedings - 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2023
PublisherInstitute of Electrical and Electronics Engineers
Pages55-66
Number of pages12
ISBN (Electronic)979-8-3503-4793-7
ISBN (Print)979-8-3503-4794-4
DOIs
Publication statusPublished - 9 Aug 2023
Event53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2023 - Porto, Portugal
Duration: 27 Jun 202330 Jun 2023
Conference number: 53

Conference

Conference53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2023
Abbreviated titleDSN 2023
Country/TerritoryPortugal
CityPorto
Period27/06/2330/06/23

Keywords

  • Network Planning
  • Recovery
  • SDN
  • TSN

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