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-2 | Engels |
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Titel | Proceedings - 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2023 |
Uitgeverij | Institute of Electrical and Electronics Engineers |
Pagina's | 55-66 |
Aantal pagina's | 12 |
ISBN van elektronische versie | 979-8-3503-4793-7 |
ISBN van geprinte versie | 979-8-3503-4794-4 |
DOI's | |
Status | Gepubliceerd - 9 aug. 2023 |
Evenement | 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2023 - Porto, Portugal Duur: 27 jun. 2023 → 30 jun. 2023 Congresnummer: 53 |
Congres
Congres | 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2023 |
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Verkorte titel | DSN 2023 |
Land/Regio | Portugal |
Stad | Porto |
Periode | 27/06/23 → 30/06/23 |