Online Rerouting and Rescheduling of Time-Triggered Flows for Fault Tolerance in Time-Sensitive Networking

Feng, Zuyong; Gu, Zonghua; Yu, Haichuan; Deng, Qingxu; Niu, Linwei

doi:10.1109/tcad.2022.3197523

Cited by 12 publications

(10 citation statements)

References 30 publications

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“…However, synthesizing a problem that minimizes CF (DV , S , VL) is a non-deterministic polynomial (NP-hard) problem, making it impractical to find an optimal solution in a finite time for large-scale networks. To address this problem, the JTTPS algorithm iteratively solves TT flows one by one and constructs topology solutions that minimize the cost Δ k CF in Equation (11).…”

Section: Jttps-ttmentioning

confidence: 99%

“…Feng et al. [11] presented an online recovery algorithm for TT traffic from the perspective of incremental reconfiguration and incorporating Frame Replication and Elimination for Redundancy(FRER), which further improved the probability of successful reconfiguration. Jonathan et al.…”

Section: Introductionmentioning

confidence: 99%

“…Kong et al [10] proposed an ILP-based run-time joint routing and scheduling solution for post-failure TT traffic recovery, which fully considered the link failure and used the Maximum Transmission Unit (MTU) as the scheduling time slot size to simplify the solution. Feng et al [11] presented an online recovery algorithm for TT traffic from the perspective of incremental reconfiguration and incorporating Frame Replication and Elimination for Redundancy(FRER), which further improved the probability of successful reconfiguration. Jonathan et al [12]improved the load factor of the reconfigured network through an offensive traffic planning strategy.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing low‐priority traffic reconfiguration designs in mixed‐critical avionics networks

Xiong

2023

IET Communications

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In avionics system networking, mixed‐critical networks are commonly used to integrate system applications with different levels of guarantees for various traffic classes on a shared resource platform, effectively addressing issues such as cabling, cost, weight, volume, power consumption, etc. However, current fault recovery and reconfiguration techniques mainly focus on high‐priority time‐triggered (TT) traffic, often neglecting the impact of TT traffic reconfiguration on low‐priority traffic. This paper proposes an avionics mixed‐critical networking approach based on the IEEE 802.1CB protocol, which is shown to be more effective for the post‐failure recovery of Rate‐Constrained (RC) traffic (a typical type of low‐priority traffic in TT networks). Furthermore, an elastic reconfiguration algorithm is presented to ensure quality of service for RC traffic during and after reconfiguration, preventing arbitrary application and traffic shutdown triggered by TT traffic reconfiguration. Simulations demonstrate that the proposed approach and algorithm can significantly improve the post‐failure recovery ratio of RC traffic, as well as guarantee quality of service and network throughput of RC traffic during and after reconfiguration to a certain extent.

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Section: Jttps-ttmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing low‐priority traffic reconfiguration designs in mixed‐critical avionics networks

Xiong

2023

IET Communications

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“…Performance evaluation: We compared our proposed method (JORTP) with the currently mature IR method (JILP) [3]. The experiments were conducted on the Orion CEV topology, which consists of 31 end systems and 15 switches with 100 TT flows whose parameters can be found in [9].…”

Section: Tabu Search-based Optimizationmentioning

confidence: 99%

“…Consequently, the reconfiguration of TT traffic becomes a complex trade-off problem involving efficiency, success rate, and reconfiguration quality. The prevailing reconfiguration strategy, incremental reconfiguration (IR) [3][4][5], leverages the remaining network bandwidth for traffic reconfiguration when introducing new traffic or addressing failures, without altering the schedule of original TT traffic. However, our prior research [6] revealed that IR, particularly in the context of joint routing and scheduling, falls short of achieving millisecond-level efficiency in real-world deployments.…”

mentioning

confidence: 99%

Joint optimization of reconfiguration and traffic planning in time‐triggered networks

Li,

Xiong

2023

Electronics Letters

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The existing research on time‐triggered network reconfiguration neglects redundant traffic states, posing a challenge in effectively balancing traffic performance and reconfiguration efficiency in practical deployments. In response to these limitations, a novel joint optimization approach is introduced for reconfiguration and traffic planning. Initially, the efficiency of reconfiguration solutions is enhanced by sacrificing the delay performance of redundant flows and prioritizing compressed yet tailored constraints. Subsequently, a tabu‐based optimization algorithm is employed to provide more flexible enhancements to the delay performance of network‐wide traffic. The outcomes demonstrate that the proposed method significantly outperforms incremental reconfiguration, improving the solution efficiency of the reconfiguration recovery phase by over 75.38%. Furthermore, through multiple rounds of reconfiguration, the approach achieves an 8.38% improvement in the average delay performance of all flows, even with redundant flows sacrificed.

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Online simulation task scheduling in cloud manufacturing with cross attention and deep reinforcement learning

Chen,

Zhang,

Laili

et al. 2024

J Intell Manuf

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Online Rerouting and Rescheduling of Time-Triggered Flows for Fault Tolerance in Time-Sensitive Networking

Cited by 12 publications

References 30 publications

Enhancing low‐priority traffic reconfiguration designs in mixed‐critical avionics networks

Enhancing low‐priority traffic reconfiguration designs in mixed‐critical avionics networks

Joint optimization of reconfiguration and traffic planning in time‐triggered networks

Online simulation task scheduling in cloud manufacturing with cross attention and deep reinforcement learning

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