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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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Samenvatting

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.
Originele taal-2Engels
TitelProceedings - 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2023
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's55-66
Aantal pagina's12
ISBN van elektronische versie979-8-3503-4793-7
ISBN van geprinte versie979-8-3503-4794-4
DOI's
StatusGepubliceerd - 9 aug. 2023
Evenement53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2023 - Porto, Portugal
Duur: 27 jun. 202330 jun. 2023
Congresnummer: 53

Congres

Congres53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2023
Verkorte titelDSN 2023
Land/RegioPortugal
StadPorto
Periode27/06/2330/06/23

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