Cyclic Queuing and Forwarding (CQF) is a key Time-Sensitive Networking (TSN) shaping mechanism that ensures bounded latency using a simple gate control list (GCL). Recently, variants of CQF, including Cycle Specific Queuing and Forwarding (CSQF) and Multi Cyclic Queuing and Forwarding (MCQF), have emerged. While popular TSN mechanisms such as the Time-Aware Shaper (TAS), Asynchronous Traffic Shaper (ATS), Credit-Based Shaper (CBS), and Strict Priority (SP) have been extensively studied, cyclic shapers have not been thoroughly evaluated. This paper presents a comprehensive analysis of CQF, CSQF, and MCQF, providing insights into their performance. We quantify delays through simulations and quantitative analysis on both synthetic and realistic networks. For the first time, we introduce an open-source OMNeT++ and INET4.4 based framework capable of modeling all three cyclic shaper variants. Our tool facilitates the validation of new algorithms and serves as a benchmark for cyclic shapers. Our evaluations reveal that MCQF supports diverse timing requirements, whereas CSQF, with its additional queue, often results in larger delays and jitter for some TT flows compared to CQF. Additionally, CSQF does not demonstrate significant advantages in TSN networks where propagation delays are less critical than in wide-area networks (WANs).

Time-Sensitive Networking (TSN) supports multiple traffic types with diverse timing requirements, such as hard real-time (HRT), soft real-time (SRT), and Best Effort (BE) within a single network. To provide varying Quality of Service (QoS) for these traffic types, TSN incorporates different scheduling and shaping mechanisms. However, assigning traffic types to the proper scheduler or shaper, known as Traffic-Type Assignment (TTA), is a known NP-hard problem. Relying solely on domain expertise to make these design decisions can be inefficient, especially in complex network scenarios. In this paper, we present a proof-of-concept highlighting the advantages of a learning-based approach to the TTA problem. We formulate an optimization model for TTA in TSN and develop a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) model, called “TTASelector”, to assign traffic types to TSN flows efficiently. Using synthetic and realistic test cases, our evaluation shows that TTASelector assigns a higher number of traffic types to HRT and SRT flows compared to the state-of-the-art Tabu Search-based metaheuristic method.

Cyclic Queuing and Forwarding (CQF) is an increasingly adopted mechanism in Time-Sensitive Networking (TSN) for bounding end-to-end delays through fixed-length cycles with alternating transmission queues. While timing guarantees for CQF flows are well established under both time-triggered (TT) and event-triggered (ET) implementations, the worst-case interference that CQF may impose on other traffic classes in mixed-criticality TSN networks remains an open problem. This challenge is exacerbated by the structured, non-work-conserving behavior of CQF and the complexity of its interaction with heterogeneous TSN scheduling mechanisms such as TAS, CBS, and SP. This paper presents the first formal framework for quantifying the worst-case interference caused by CQF on other schedulers under both TT- and ET-based implementations. We propose a network-calculus-based CQF real-time interface abstraction that models the residual service available to coexisting traffic. We formally derive closed-form upper bounds on CQF-induced interference, explicitly capturing bidirectional interactions between CQF and both higher- and lower-priority traffic classes. These bounds can be modularly and seamlessly integrated into existing schedulability analyses, enabling scalable and compositional timing verification in hybrid TSN architectures. Extensive evaluations on synthetic benchmarks and realistic TSN configurations demonstrate the analytical effectiveness, scalability, and practical applicability of the proposed framework in certifying end-to-end guarantees in mixed-criticality TSN systems.

Cyclic queuing and forwarding (CQF) is a time-sensitive networking (TSN) shaping mechanism that provides bounded latency and deterministic Quality of Service (QoS). However, CQF’s use of a single cycle restricts its ability to support TSN traffic with diverse timing requirements. multi-cyclic queuing and forwarding (Multi-CQF) is a new and emerging TSN shaping mechanism that uses multiple cycles on the same egress port, allowing it to accommodate TSN flows with varied timing requirements more effectively than CQF. Despite its potential, current Multi-CQF configuration studies are limited, leading to a lack of comprehensive research, poor understanding of the mechanism, and limited adoption of Multi-CQF in practical applications. Previous work has shown the impact of time injection (TI), defined as the start time of time-triggered (TT) flows at the source node, on CQF queue resource utilization. However, the impact of TI has not yet been explored in the context of Multi-CQF. This article introduces a set of constraints and leverages domain-specific knowledge (DSK) to reduce the search space for Multi-CQF configuration. Building on this foundation, we develop an open-source genetic algorithm (GA) and a hybrid GA-simulated annealing (GASA) approach to efficiently configure Multi-CQF networks and introduce TI in Multi-CQF to enhance schedulability. Experimental results show that our proposed algorithms significantly increase the number of scheduled TT flows compared to the baseline simulated annealing (SA) model, improving scheduling by an average of 15%. Additionally, GA-SA (GASA) achieves a 20% faster convergence rate and lower time complexity, outperforming the SA model in speed, and efficiency.

Different scheduling mechanisms in Time Sensitive Networking (TSN) can be integrated together to design and support complex architectures with enhanced capabilities for mixed critical networks. Integrating Frame Preemption (FP) with Credit-Based Shaper (CBS) and Gate Control List (GCL) opens up different modes and configuration choices resulting in a complex evaluation of several possibilities and their impact on the Quality of Service (QoS). In this paper, we implement and quantify the integration of preemptive CBS with GCL by incorporating FP into the architecture. Our experiments show that the end-to-end delay of Audio Video Bridging (AVB) flows shaped by CBS reduces significantly (up to 40%) when AVB flows are set to preemptable class. We further show that the jitter of Time Triggered (TT) traffic remains unaffected in "with Hold/Release" mode. Furthermore, we propose to introduce Guardband (GB) in the "without Hold/Release" to reduce the jitter of the TT flow. We compare all the different integration modes, starting with CBS with GCL, extending it further to FP. We evaluate all feasible combinations in both synthetic and realistic scenarios and offer recommendations for practical configuration methods.

The selection of a Time-Sensitive Networking (TSN) shaping mechanism is a crucial design decision that impacts the Quality of Service (QoS) of applications and configuration complexity. However, current research has mainly evaluated TSN shapers individually, despite them being designed to work together in an egress port. Hence, the lack of investigation of the mixed TSN shaping mechanism is a major limitation of the current state of the art. Combined TSN traffic shaping provides greater flexibility to improve QoS than individual shapers, making it particularly beneficial for real-time applications. This paper aims to bridge this research gap by implementing the Asynchronous Traffic Shaper (ATS) in a plug-and-play manner, enabling its use individually or in combination with other TSN shapers. We propose various models of mixed TSN shaper architectures and implement the frozen and non-frozen credit behavior of the Time Aware Shaper (TAS) + Credit Based Shaper (CBS) during the guard band (GB) using OMNeT++. Furthermore, we compare the simulation results of ATS and CBS with the Network Calculus (NC) upper bounds. Our results indicate that the simulated delays (SMDs) were significantly lower than the theoretical worst-case delays (WCDs) obtained from the NC, indicating the need for tighter theoretical upper bounds, particularly for higher network loads. To the best of our knowledge, we are the first to provide simulation-based performance analysis of the combined$\text{TAS}+\text{ATS}+\text{CBS}$and$\text{TAS}+\text{ATS}+\text{Strict}$Priority (SP) architecture. Overall, this paper highlights the benefits of combining TSN shapers and encourages further research into the potential advantages of utilizing multiple shapers simultaneously to decrease reliance on TAS and CBS.

The integration of Time-Sensitive Networking (TSN) and 5G communication is crucial for achieving ultra-reliable and low-latency communication (URLLC) in various domains. This includes applications like industrial automation involving mobile and collaborative robots, as well as Industrial IoT (IIoT). However, an exhaustive performance analysis of mixed 5G-TSN networks that support the unique features of TSN and 5G is lacking. Furthermore, a deeper understanding of TSN traffic scheduling in the 5G system (5GS) is necessary. To address this gap, we propose a novel 5G-TSN Quality-of-Service (QoS) aware simulation framework that incorporates priority-based scheduling in the 5GS. Our framework integrates the latest 5G and TSN simulation libraries using OMNeT++ and provides the first-ever results of the performance analysis of a 5G-TSN converged network. We implement the 5G-TSN bridge translation mechanism and introduce a QoS mapping algorithm for our framework. Through a detailed performance evaluation, we assess the impact of 5G QoS-aware scheduling methods on the overall network performance. Our open-source framework utilizes the latest Simu5G and INET4.4 libraries and simulates two different 5G-TSN scenarios to provide comprehensive insights. The results of our study demonstrate that the performance of the 5G-TSN network is significantly influenced by the scheduling in the 5G network, as a substantial portion of the overall delay originates from the 5GS. Notably, our findings reveal that the 5G-TSN network can achieve latency values within 3ms for TSN traffic, emphasizing the need for a joint scheduling mechanism to meet URLLC requirements.