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5G 基础设施网络切片:端到端平均延迟模型与有效性评估,以减少工业 4.0 中的停机时间。

5G Infrastructure Network Slicing: E2E Mean Delay Model and Effectiveness Assessment to Reduce Downtimes in Industry 4.0.

机构信息

Department of Signal Theory, Telematics and Communications, University of Granada, 18014 Granada, Spain.

Research Center on Information and Communication Technologies, University of Granada, 18014 Granada, Spain.

出版信息

Sensors (Basel). 2021 Dec 29;22(1):229. doi: 10.3390/s22010229.

DOI:10.3390/s22010229
PMID:35009771
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8749764/
Abstract

Fifth Generation (5G) is expected to meet stringent performance network requisites of the Industry 4.0. Moreover, its built-in network slicing capabilities allow for the support of the traffic heterogeneity in Industry 4.0 over the same physical network infrastructure. However, 5G network slicing capabilities might not be enough in terms of degree of isolation for many private 5G networks use cases, such as multi-tenancy in Industry 4.0. In this vein, infrastructure network slicing, which refers to the use of dedicated and well isolated resources for each network slice at every network domain, fits the necessities of those use cases. In this article, we evaluate the effectiveness of infrastructure slicing to provide isolation among production lines (PLs) in an industrial private 5G network. To that end, we develop a queuing theory-based model to estimate the end-to-end (E2E) mean packet delay of the infrastructure slices. Then, we use this model to compare the E2E mean delay for two configurations, i.e., dedicated infrastructure slices with segregated resources for each PL against the use of a single shared infrastructure slice to serve the performance-sensitive traffic from PLs. Also we evaluate the use of Time-Sensitive Networking (TSN) against bare Ethernet to provide layer 2 connectivity among the 5G system components. We use a complete and realistic setup based on experimental and simulation data of the scenario considered. Our results support the effectiveness of infrastructure slicing to provide isolation in performance among the different slices. Then, using dedicated slices with segregated resources for each PL might reduce the number of the production downtimes and associated costs as the malfunctioning of a PL will not affect the network performance perceived by the performance-sensitive traffic from other PLs. Last, our results show that, besides the improvement in performance, TSN technology truly provides full isolation in the transport network compared to standard Ethernet thanks to traffic prioritization, traffic regulation, and bandwidth reservation capabilities.

摘要

第五代(5G)有望满足工业 4.0 的严格网络性能要求。此外,其内置的网络切片功能允许在同一物理网络基础设施上支持工业 4.0 的流量异构性。然而,就许多专用 5G 网络用例(如工业 4.0 中的多租户)而言,5G 网络切片功能在隔离程度方面可能不够。在这种情况下,基础设施网络切片是指在每个网络域中为每个网络切片使用专用且隔离良好的资源,这符合这些用例的需求。在本文中,我们评估了基础设施切片在提供工业专用 5G 网络中生产线(PL)之间隔离的有效性。为此,我们开发了一个基于排队论的模型来估计基础设施切片的端到端(E2E)平均分组延迟。然后,我们使用该模型比较了两种配置的 E2E 平均延迟,即每个 PL 都有专用的基础设施切片和专用资源,以及使用单个共享基础设施切片来为 PL 的性能敏感流量提供服务。我们还评估了使用时间敏感网络(TSN)替代裸以太网来提供 5G 系统组件之间的第二层连接。我们使用基于所考虑场景的实验和模拟数据的完整和现实设置。我们的结果支持基础设施切片在不同切片之间提供性能隔离的有效性。然后,为每个 PL 使用专用的切片和隔离的资源可能会减少生产停机时间和相关成本,因为 PL 的故障不会影响来自其他 PL 的性能敏感流量感知到的网络性能。最后,我们的结果表明,除了性能的提高,TSN 技术通过流量优先级、流量调节和带宽预留功能,真正在传输网络中提供了与标准以太网相比的完全隔离。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/20855610d0bc/sensors-22-00229-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/efa8164e0d5e/sensors-22-00229-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/2d45366273b4/sensors-22-00229-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/60d60f4a8cf1/sensors-22-00229-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/d79e16a1711e/sensors-22-00229-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/ccc67f62c722/sensors-22-00229-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/94c4ae56e96a/sensors-22-00229-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/d097737d98d8/sensors-22-00229-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/d1d45092b63c/sensors-22-00229-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/7f1d807f79e5/sensors-22-00229-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/20855610d0bc/sensors-22-00229-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/efa8164e0d5e/sensors-22-00229-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/2d45366273b4/sensors-22-00229-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/60d60f4a8cf1/sensors-22-00229-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/d79e16a1711e/sensors-22-00229-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/ccc67f62c722/sensors-22-00229-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/94c4ae56e96a/sensors-22-00229-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/d097737d98d8/sensors-22-00229-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/d1d45092b63c/sensors-22-00229-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/7f1d807f79e5/sensors-22-00229-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/8749764/20855610d0bc/sensors-22-00229-g010.jpg

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