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

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 PLs in an industrial private 5G network. To that end, we develop a queuing theory-based model to estimate the 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 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.

Estimation of 5G Core and RAN End-to-End Delay through Gaussian Mixture Models

“…In what follows, we compare the PDF plots of the estimated GMM PDF for three and eight components with the empirical dataset PDF. The impact of changing the sample rates (1/100, 1/50, 1/20, 1/10, 1/5, 1/2, and 1) and the number of components (2,3,5,8,10,12) on the GMM's accuracy MSE and EM computation time is characterized by running the estimation methodology a thousand of times and averaging the results. The average MSE and computation time are plotted for each scenario.…”

“…Network Calculus-based (NC) models were also proposed in the literature [9][10][11]. NC models allow the computation of a delay bound of a flow traversing the network [12]. Although delay bounds are useful for time-sensitive scenarios, such as the parameterization of timeout values, they do not provide a high level of description as the one obtained from the estimation of its distribution.…”

A Stochastic Network Calculus (SNC)-Based Model for Planning B5G uRLLC RAN Slices

“…However, if more complex distributions are considered [10], computing the CDF using queueing theory is mathematically intractable. In such cases, queueing theory-based models can only provide average values for the packet transmission delay.…”

Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models

“…In the 5G networks context, the E2E delay refers to the time taken for data to travel from the source to the destination, encompassing all the intermediary processes. E2E delay has been employed as a key performance indicator (KPI) parameter that directly impacts the user experience quality and latency-sensitive applications [9,10]. The URLLC supported by 5G technology opens up new opportunities for implementing applications such as autonomous vehicles, virtual reality, securing the Internet of Things (IoT), and industrial automation [11].…”