Improving TSN Simulation Accuracy in OMNeT++: A Hardware-Aligned Approach

Time-sensitive networking (TSN) is a new industrial communication technology being actively promoted by the international industry. The deterministic transmission of time-triggered (TT) traffic in TSN is guaranteed by Time-aware shaper (TAS), which is defined in IEEE 802.1Qbv. However, if the TSN technology needs to be applied to the existing network environment, the deterministic transmission is difficult to achieve. This paper designs and implements a TAS to realizes deterministic transmission of TT streams in TSN. Experimental results show that the proposed TAS can achieve good shaping effects and can meet the transmission requirements of the TT streams.

This article contains the problem statement and numerical estimations regarding design of high-performance Time Sensitive Networking (TSN) switching nodes. Performance metrics and delay components are introduced and the impact of each design choice on them is investigated. An architecture supporting the TSN features of Credit Based Shaping (CBS) and Time Aware Shaper (TAS) in a high-performance input queued switch is presented, together with supporting scheduling algorithm. The paper additionally contains a set of recommendations for design of high-capacity TSN switches.

With the interconnection of distributed devices from different industrial automation networks, heterogeneous traffics with different Quality of Service (QoS) requirements increase dramatically. Shapers in Time-Sensitive Networking (TSN) enable the coexistence of different industrial traffics within a single network infrastructure. In this paper, with full consideration of the time-criticality of different industrial traffics and shaping mechanisms, we propose a network calculus-based modeling scheme for the widely-used Credit Based Shaper (CBS) and Time Aware Shaper (TAS). Then, we analyze the QoS of industrial traffics with different real-time characteristics by CBS and TAS, respectively. To evaluate the accuracy of network calculus-based modeling scheme, we compare our theoretical analysis with simulations, wherein a compactness function is defined to describe QoS compactness. Simulation results verify the effectiveness of proposed modeling scheme, by which we further validate both CBS and TAS can satisfy the QoS of traffics for different industrial automation scenarios.

Time-sensitive networking (TSN) strives to provide an ultralow-latency real-time deterministic network for time-critical traffic using the time-aware shaper (TAS) mechanism. For this purpose, methods for routing and scheduling the time-critical flows must be specified. However, this is an NP-hard problem. Although several prior studies have suggested constraint programming (CP)-based approaches, these methods fail to provide a reasonable runtime due to the complexity of the problem. Motivated by this, we propose a TAS co-optimization (TACO) framework that solves the TAS scheduling and routing problem in TSN with a reasonable runtime. As an alternative to CP-based approaches, TACO considers a metaheuristic approach to co-optimize routing, scheduling order, and transmission timing. However, joint optimization through a metaheuristic algorithm is challenging due to the heterogeneous search spaces of the subproblems. Therefore, TACO carefully integrates the search spaces into a single domain and optimizes routing and TAS scheduling jointly with its heuristic algorithm. We evaluate TACO in various industrial networking scenarios to demonstrate that TACO achieves up to an 88% increase in the scheduling success rate with a good convergence rate and an overall low latency/jitter compared to other approaches.

In order to improve the real-time performance of Industrial Internet, this paper proposes a network communication delay optimization scheme that based on the IEEE 802.1Qbv protocol of time sensitive network (TSN). The scheme distinguishes the network data types with different time delay requirements, and proposes a TAS-EWRR scheduling algorithm in the data link layer of OSI model. The algorithm combines with time aware shaper (TAS), stream reservation protocol (SRP), frame preemption mechanism and enhanced weighted round robin (EWRR) algorithm. At the same time, a shortest path algorithm based on delay is combined at the network layer to realize the optimization of the delay of the entire network.KeywordsIndustrial internetDelayTSNTASShortest path

Smart substations are an important trend in substation construction. With increasing data traffic, it is difficult for the traditional Ethernet network to meet the real-time requirements of control information in smart substations. Hence, in this paper, a deterministic network architecture for substations based on time-sensitive networks (TSN) has been developed in order to realize the domain-wide time synchronization and efficient real-time communication of the “three-layer and two-network” model in smart substations. Furthermore, a design scheme for substation automation equipment based on TSN is proposed. The proposed device realizes the timely transmission of real-time control information packets by utilizing the Earliest TxTime First (ETF) Qdisc technology of Linux and the timing sending capability of Intel 210 NIC. Furthermore, it collaborates with the time-aware shaper (TAS) traffic scheduling mechanism of TSN switches to ensure the end-to-end deterministic delay of time-sensitive traffic. As a result, it provides efficient real-time communication services with low latency and jitter for smart substation automation systems.

As new networking paradigms emerge for different networking applications, e.g., cyber-physical systems, and different services are handled under a converged data link technology, e.g., Ethernet, certain applications with mission critical traffic cannot coexist on the same physical networking infrastructure using traditional Ethernet packet-switched networking protocols. The IEEE 802.1Q Time Sensitive Networking (TSN) Task Group is developing protocol standards to provide deterministic properties, i.e., eliminates non-deterministic delays, on Ethernet based packet-switched networks. In particular, the IEEE 802.1Qcc, centralized management and control, and the IEEE 802.1Qbv, Time-Aware Shaper (TAS), can be used to manage and control Scheduled Traffic (ST) streams with periodic properties along with Best-Effort (BE) traffic on the same network infrastructure. We investigate the effects of using the IEEE 802.1Qcc management protocol to accurately and precisely configure TAS enabled switches (with transmission windows governed by Gate Control Lists (GCLs) with Gate Control Entries (GCEs)) ensuring ultra-low bounded latency, zero packet loss, and minimal jitter for ST TSN traffic. We examine both a centralized network/distributed user model (hybrid model) and a fully-distributed (decentralized) 802.1Qcc model on a typical industrial control network with the goal of maximizing the number of ST streams.

Introduction: Ethernet-based automotive networks require additional mechanisms to ensure low latency for time-sensitive streams. The Time-Sensitive Networking (TSN) task group for real-time systems has proposed several mechanisms, including the time-aware shaper (TAS), to provide real-time performance. TAS enables the setting of separate transmission time windows at output ports for different queues, thereby preventing interference from other priorities. The primary objective of this study is to determine the worst-case end-to-end delay of class-based TAS streams in vehicular networks with less conservative error, which can then be used to guide the reverse design of TAS parameters. Method: This study employs Compositional Performance Analysis (CPA) to compute the worst-- case end-to-end (E2E) delay of class-based TAS streams at the data link layer. The optimized CPA results are compared with pre-optimization calculations and simulation outcomes. Result: The optimized CPA calculation reduces the end-to-end delay by an average of 42.6% for two-hop TAS streams and 57.8% for three-hop TAS streams compared to the pre-optimization results. Discussion: Building on the existing CPA method, this study enhances E2E worst-case delay analysis and reduces conservative error. Additionally, synchronization errors and extreme operational scenarios are discussed. However, the methodology proposed in this study is only applicable to scenarios with aligned gating windows. Conclusion: All simulation results for TAS streams fall within the bounds predicted by the CPA calculation. The optimized CPA analysis of the worst-case end-to-end delay greatly reduces conservative error. It can be used to validate the appropriateness of the class-based gate control parameter configurations.

The paper considers the problem of modeling time-critical networks in order to study the effectiveness of various real-time traffic management methods. Relevance. Real-time network modeling is an active area of research in computer science and information technology. Research in this area is aimed at developing methods and algorithms for efficient real-time data transmission. The work is devoted to modeling time-sensitive networks using various real-time traffic management methods. Goal. The purpose of the work was to analyze existing real-time network standards, develop a simulation model of a real-time network, and investigate the effectiveness of various real-time traffic management methods. Research methods. The research methods are based on modern theories of mathematical modeling, simulation modeling, and telecommunications standards, namely Time-Sensitive Networking (TSN). The results. The standards of real-time networks were thoroughly investigated. Detailed attention was paid to TSN (Time Sensitive Network) technology, the problem of achieving synchronization in such networks and various methods of traffic generation were investigated; the obtained analytical results were checked for compliance with the TSN network specification. OMNeT++ software was used for modeling real-time networks. End-to-end delay graphs were constructed for each traffic generation method under different to conditions of real-time networks. Conclusions. The paper considered TSN standard networks for reliable real-time communication and increase versatility in industrial networks. The problem of achieving synchronization of devices in the TSN network was investigated, various methods of traffic planning were considered, the importance of using end-to-end switching technology was established, a simulation model of the TSN network was developed.

IEEE 802.1 Time-Sensitive Networking (TSN) assures a guaranteed data delivery with limited latency, low jitter, and amazingly low loss of data in handling time-critical traffic. TSN handles different quality of service (QoS) requirements and frame preemption is one of the key features of TSN. In the healthcare sector networking technology preferred by large organizations uses an enormous number of nodes, and thereby, the complexity of the network increases. Since the priority of the medical data varies at times based on the patient's health, dynamic traffic scheduling mechanisms are preferred. To improve the efficiency of the network, the software-defined access mechanism is used to control the network switches and bridges in the time-sensitive network. This work uses reinforcement learning to identify and eliminate the bridges dropping packets, and the alternative path is used to schedule the real-time data traffic. It is perceived that it performs well for the time-critical data in congestion network, increases the throughput, and reduces latency.

Industrial automation, Augmented Reality (AR), Virtual Reality (VR), Robotics, and many other applications need accurate time and data delivery with strict deadlines. The IEEE 802.1 Time-Sensitive Networking (TSN) group defines a set of tools for deterministic communication with high reliability. Although TSN has started from wired (Ethernet) networks, the newer generation wireless technologies, especially Wi-Fi 6/6E and 5G, offer capabilities to extend and enhance TSN operation over the wireless medium. The 3rd Generation Partnership Project (3GPP) Release 16 supports Ultra-Reliable Low-Latency Communications (URLLC) and introduces the capabilities for integrating 5G with TSN networks motivated by the requirements of time-critical industrial systems. This paper presents and evaluates an industrial control system enabled by 5G and TSN capabilities. We describe a TSN over 5G testbed developed with 3GPP Release 15 hardware and discuss the measurement methodology along with Key Performance Indicators (KPIs) required for meeting timing requirements for an industrial control application. We also explore the configuration of TSN over 5G systems and methodologies needed to characterize the Quality of Experience (QoE) for industrial use cases.

Time-Sensitive Networking (TSN) is vital to enable time-critical deterministic communication, especially for applications with industrial-grade requirements. IEEE TSN standards are key enablers to provide deterministic and reliable operation on top of Ethernet networks. Much of the research is still done in simulated environments or using commercial TSN switches lacking flexibility in terms of hardware and software support. In this demonstration, we use an open Cloud testbed for TSN experimentation, leveraging the hardware features that support precise time synchronization, and fine-grained scheduling according to TSN standards. We demonstrate the setup and operation of a Linux-based TSN network in the testbed using our modular Centralized Network Configuration (CNC) controller prototype. With our CNC we are able to quickly initialize the TSN bridges and end nodes, as well as manage their configurations, modify schedules, and visualize overall network operation in real-time. The results show how the TSN features can be effectively used for traffic management and resource isolation.

This paper addresses Wi-Fi 6 time-aware scheduling based on Target Wake Time (TWT) in a way that is compatible with the Time Sensitive Networking (TSN) Time-aware Shaper (TAS). The paper proposes novel mechanisms and describes a proof-of-concept implementation. The implemented solution has been evaluated in terms of latency and jitter in a testbed with operational Wi-Fi 6 equipment. Results achieved in terms of latency and jitter show that our proposal provides a way to isolate critical traffic from other types of traffic in an effective and flexible way.

5th-Generation (5G) and Time-Sensitive Networking (TSN) are regarded as competitive new technologies for future industrial networks; 5G-TSN collaboration transmission has drawn more attention because it can provide a guarantee of low-latency, ultra-reliable and deterministic transmission for time-critical automation applications. However, the methodologies of resource scheduling mechanisms in 5G and Time-Sensitive Networking (TSN) are quite different, which may lead to an inefficient Quality of Service (QoS) guarantee for deterministic transmission across 5G and TSN. Therefore, an efficient 5G-TSN joint scheduling algorithm based on Deep Deterministic Policy Gradient (DDPG) is proposed and analyzed in this article. The proposed algorithm takes both 5G radio channel information and the Gate Control List (GCL) state in the TSN domain into consideration, aiming to provide a latency guarantee for time-triggered applications across 5G and TSN as well as a throughput guarantee for video applications in 5G systems. The simulation results compare the latency and throughput performance of the proposed joint scheduling algorithm with several traditional 5G scheduling algorithms; meanwhile, several GCL setting methods are given to verify the impacts on latency and throughput performance within the proposed algorithm. The simulation results demonstrate that the proposed DDPG-based joint scheduling algorithm can significantly enhance the multi-application-carrying capability of 5G-TSN collaboration architecture.

The application of a program-based control method using an Electronic Control Unit (ECU) for mechanical control has improved the reliability and performance of vehicles. However, vehicle network structures are becoming increasingly complex. Consequently, the current central gateway method is in the process of changing from a domain-based structure to a zone-based network. The Institute of Electrical and Electronics Engineers IEEE 802.1 group announced Time-Sensitive Networking (TSN) in line with the changing vehicle network system. TSN uses the IEEE 802.1Qav, IEEE 802.1Qbv, and IEEE 802.1Qbu protocols to achieve bounded low latency. Estimating the guard band area is essential when applying the IEEE 802.1 Qav protocol. This is because if the audio, video, and text frames are long or the transmission time is insufficient, the scheduled traffic area, where the control data are transmitted, might be invaded by the guard band area. A preemption function can be used to prevent such problems; however, to do so, additional processing time is required to divide or recombine frames. In addition, it is necessary to separate the divided frames and the required header for the re-assembly process. Subsequently, the preemption function can be used to minimize the guard band area, reduce the number of preemption occurrences, and increase the efficiency of the network. In this study, to solve the above-mentioned problem, we propose a method to explore the frame preemption and to reduce the number of preemptions that may occur in a specific situation. As a result, the number of preemption occurrences could be reduced by 87% by applying the proposed method.