This work presents a comprehensive overview of the TSN deployment lifecycle, current challenges, limitations of existing tools, and future research directions for TSN deployment and management, and identifies key research gaps from a deployment perspective and provides guidance for the development of next-generation deployable TSN networks.
Abstract
Time-Sensitive Networking (TSN) provides deterministic guarantees and bounded latency for safety-critical applications across different domains. While the IEEE 802.1 TSN standards provide a comprehensive set of mechanisms for time synchronization, shaping, scheduling, resource reservation, and reliability, deploying TSN in real-world applications remains a complex, multi-step engineering task. Existing research primarily focuses on optimizing individual objectives, such as increasing schedulability, reducing latency, accelerating solution generation, or improving performance analysis. However, whether these extensively researched solutions can be deployed in real-world systems remains unanswered, as the assumptions made in these methodologies often differ from practical hardware constraints. Therefore, a broader, holistic view of end-to-end TSN deployment and management remains an open research question. Currently, there is a lack of a unified perspective, consensus, and coordinated research efforts focusing on a comprehensive end-to-end TSN deployment workflow. We present a comprehensive overview of the TSN deployment lifecycle, current challenges, limitations of existing tools, and future research directions for TSN deployment and management. Lastly, we identify key research gaps from a deployment perspective and provide guidance for the development of next-generation deployable TSN networks.
The lack of determinism restricts the integration of safety-critical applications into Edge–Fog–Cloud (EFC) architectures. Existing EFC schedulers are typically designed for dynamic, best-effort operation based on unmanaged resource allocation and elastic virtualization. This paradigm introduces unbounded queueing, res...
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Edge computing is a promising paradigm for deploying latency-sensitive applications (Apps) as it brings resources closer to end users. Edge Apps often adopt a microservice (MS) architecture, breaking monolithic Apps into lightweight, containerized MSs that can be dynamically and independently deployed. However, managin...
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SbDN is presented, a Multi-Agent Source-based architecture that achieves TSN-grade determinism using commodity Ethernet switches and provides two methods: Temporal Network Partitioning (TNP), which provides strict temporal isolation on pure FIFO switches, and Traffic Prioritization (TP), which leverages strict-priority...
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Serverless computing has emerged as a promising paradigm for deploying distributed cyber-physical systems (CPS), such as smart grids and industrial control applications, due to its elasticity and lightweight execution model. In these CPS settings, sensing, communication, and actuation are tightly coupled in closed-loop...
Vajiheh Farhadi· International Conference on...· 0 citations
The nascTime framework on OMNeT++/Simu5G is used to evaluate how many TSN endpoints a single 5G NR cell can bridge before per-flow QoS degrades, showing that sub-3 ms TSN deadlines may require radio-configuration changes such as configured grants or higher numerology.
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B-CUBE is proposed, a novel burst-aware 5G-TSN architecture that equips each industrial node with co-deployed TSN and 5G system bridges to enable wired, wireless, and dual-mode transmission, and designs a set of e-cient cross-domain mixed-flow scheduling algorithms.
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