Frame Preemption Research Articles

Design and implementation of a frame preemption model without guard bands for time-sensitive networking

In time-sensitive networks, the frames of time-triggered (TT) flows need to be transmitted in scheduled slots. To avoid the interference of frames from other flows to the frames of the TT flows, guard bands are generally reserved prior to scheduled slots. The time-sensitive networking (TSN) standard, IEEE 802.1Qbu, specifies a frame preemption model that enables frames of TT flows (preemption frames) to preempt frames of other flows (preempted frames). According to this model, preempted frames are divided into fragments that are transmitted in the gaps of preemption frames. Consequently, the IEEE 802.1Qbu model reduces the guard band from the longest Ethernet frame (typically 1518 bytes) to 123 bytes, which improves the bandwidth utilization and delay performance of preempted frames without causing frame disorder. However, in scenarios in which the preempted frame load is heavy, and the length is short, numerous guard bands smaller than 123 bytes are generated. These guard bands prevent the IEEE 802.1Qbu model from transmitting preempted frame fragments, resulting in a considerable decrease in bandwidth utilization. To solve this problem, we propose a novel frame preemption model without guard bands, based on a padding and splicing mechanism. While ensuring that preemption frames are transmitted according to scheduled slots, this model can transmit preempted frames within arbitrary byte gaps based on the schedule without obtaining the length of the preempted frames in advance. We compared the transmission-delay performance of the proposed model with that of the IEEE 802.1Qbu model using a theoretical analysis. The evaluation results obtained from heavily loaded preemption frame scenarios revealed that the proposed model improved link utilization by 32.8% relative to the IEEE 802.1Qbu model and reduced the transmission delay by more than one order of magnitude. Moreover, when the IEEE 802.1Qbu model fails, the proposed model still transmits 60% of preempted frames.

An efficient frame preemption algorithm for time-sensitive networks using enhanced graph convolutional network with particle swarm optimization

Numerous time-sensitive applications are being made possible by the Internet of Things (IoT) phenomenon, which calls for real-time transmission to provide communication services. Applications that depend on low latency have been made possible because of the 5G Ultra-Reliable and Low-Latency Communication (URLLC) scenario. Widely regarded as a possible paradigm for achieving the deterministic transmission guarantees for 5G, Time-Sensitive Networking (TSN) has gained much attention. TSN, on the other hand, is a hybrid traffic system that combines best-effort and time-sensitive traffic, necessitating efficient routing and frame preemption to provide a predictable and limited latency. On the other hand, time-sensitive and non-time-sensitive traffic optimization significantly broaden the issue area and increase the difficulty of establishing a solution. This research presents Enhanced Graph Convolutional Network-based Deep Reinforcement Learning (EGCN-based DRL) solutions for the joint optimization problem in real-world communication contexts. The EGCN is included in deep reinforcement learning to obtain the network's spatial dependency and enhance the generalization performance of the suggested technique (DRL). Specifically, the EGCN approximates the graph convolution kernel using the first-order Chebyshev polynomial, which reduces the complexity of the algorithm and improves the feasibility of the task. Particle Swarm Optimization (PSO) is also employed to hasten the convergence of the model training process. The objective of TSN is to provide deterministic and time-critical communication capabilities over Ethernet networks. TSN aims to enable reliable, low-latency, and synchronized communication between devices, ensuring that time-sensitive data and control messages are delivered with high precision and determinism. The suggested EGCN-based DRL algorithm surpasses cutting-edge techniques in terms of average end-to-end latency, and it also converges quickly, according to numerical simulations.

Data Flow Control for Network Load Balancing in IEEE Time Sensitive Networks for Automation

IEEE time sensitive networks (TSN) offer redundant paths for automation networks that are essential preconditions for network load balancing (NLB) distribution. They also provide several traffic shapers and schedulers with different impacts on the data flow control. The selection of the right traffic shaper or scheduler for an automation network is challenging. Their influence depends on various network parameters such as network extension, network cycles, application cycles, and the amount of data per traffic class and network cycle. In this study, data flow control for NLB in automation TSN using different traffic shapers and schedulers was investigated. The effects of the network parameters on the shapers and schedulers were derived and imported into the data flow control model of the automation network. The sample networks were simulated, and performance comparisons were made. The results show that the enhancements for scheduled traffic (EST), strict priority queuing (SPQ), and the combination of SPQ with frame preemption (FP) are better scheduler selections in connection with larger networks, fast network cycles, and fast application cycles. The cyclic queuing and forwarding (CQF) shaper and asynchronous traffic shaper (ATS) are rather an alternative for load control in small networks or in conjunction with slow applications.

Dynamic Path Planning Using Software-Defined Access in Time-Sensitive Healthcare Communication Network

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.

Implications of Various Preemption Configurations in TSN Networks

Time-sensitive networking (TSN) is a set of standards developed by the IEEE 802.1 task group to support high-bandwidth and low-latency predictable network communication. Configuration of TSN networks is a daunting task as there are several novel features in TSN and numerous possibilities can arise by considering combinations of these features. This letter investigates the impact of various unexplored frame-preemption configurations in TSN networks on the timing behaviors of various traffic classes. These configurations correspond to different combinations of settings in TSN with respect to the credit-based shaper, time-aware shaper, hold and release mechanism, and frame preemption. This letter provides the evaluation of these configurations by means of an analytical discussion and simulation experiments. The results and implications discussed in this letter can serve as useful guidelines for the network designers when configuring TSN networks with regards to the addressed features.

Neuro-Fuzzy-Based Frame Pre-Emption Using Time-Sensitive Networking for Industrial Ethernet

IEEE802.1 Time-Sensitive Networking (TSN) makes it conceivable to convey the data traffic of time as well as critical applications using Ethernet shared by different applications having diversified Quality of Service (QoS) requirements for both TSN and non-TSN. TSN assures a guaranteed data delivery with limited latency, low jitter, and amazingly low loss of data for time-critical traffic. By holding networking resources for basic traffic, and applying different queuing and traffic shaping strategies, TSN accomplishes zero congestion loss for basic time-critical traffic. In proposed system, backpropagation algorithm is used to train the training set and fuzzy inference system methodologies such as Mamdani fuzzy inference system which has fuzzy inputs and fuzzy outputs, Sugeno FIS which has fuzzy inputs and a crisp output and adaptive-network-based fuzzy inference system has obtained from the neural network and fuzzy logic. The proposed system uses neuro-fuzzy techniques to handle frame pre-emption and reduces the time taken for decision making. It presents a decision making process using the traffic class.

A novel frame preemption model in TSN networks

This paper identifies a limitation in the frame preemption model in the TSN standard (IEEE 802.1Q-2018), due to which high priority frames can experience significantly long blocking delays, thereby exacerbating their worst-case response times. This limitation can have a considerable impact on the design, analysis and performance of TSN-based systems. To address this limitation, the paper presents a novel and more efficient frame preemption model in the TSN standard that allows over 90% reduction in the maximum blocking delay leading to lower worst-case response times of high priority frames compared to the frame preemption model used in the existing works. The paper also shows that the improvement becomes even more significant in multi-switch TSN networks. In order to evaluate the effects of preemption, the paper performs simulations by enabling and disabling preemptions as well as enabling and disabling the Hold/Release mechanism supported by TSN. Furthermore, the paper performs a comparative evaluation of the two models of frame preemption in TSN using simulations. The evaluation shows that the maximum response times of high priority frames can be significantly reduced with very small impact on the response times of lower priority frames. The paper also shows the improvement in the maximum response times of higher priority frames using an automotive industrial use case that employs a multi-hop TSN network for on-board communication.

Design Aspects of Low-Latency Services with Time-Sensitive Networking

The aim of Time-Sensitive Networking (TSN) is to provide determinism to IEEE 802 networks. With TSN, data streams of general background applications can share the same standards-based network infrastructure without impacting time-critical applications, also leading to cost savings by using a single network infrastructure. Deterministic ultra-low latency increases efficiency for operations that require consistent message exchange. The most stringent delay requirements are specified for industrial and fronthaul network applications; thus, we demonstrate network engineering aspects in these two networking scenarios. In industrial networking, deterministic delays are important, especially for real-time control processes. In fronthaul networks, the timely delivery of data units with minimum delay variations is required for efficient operation. In this article, we present two mature TSN tools: time gated queuing and frame preemption. Then we highlight their operational and design aspects when using them in industrial and fronthaul networks, investigating their applicability through different simulation scenarios.