Just a Second -- Scheduling Thousands of Time-Triggered Streams in Large-Scale Networks

Deterministic real-time communication with bounded delay is an essential requirement for many safety-critical cyber-physical systems, and has received much attention from major standardization bodies such as IEEE and IETF. In particular, Ethernet technology has been extended by time-triggered scheduling mechanisms in standards like TTEthernet and Time-Sensitive Networking. Although the scheduling mechanisms have become part of standards, the traffic planning algorithms to create time-triggered schedules are still an open and challenging research question due to the problem's high complexity. In particular, so-called plug-and-produce scenarios require the ability to extend schedules on the fly within seconds. The need for scalable scheduling and routing algorithms is further supported by large-scale distributed real-time systems like smart energy grids with tight communication requirements. In this paper, we tackle this challenge by proposing two novel algorithms called Hierarchical Heuristic Scheduling (H2S) and Cost-Efficient Lazy Forwarding Scheduling (CELF) to calculate time-triggered schedules for TTEthernet. H2S and CELF are highly efficient and scalable, calculating schedules for more than 45,000 streams on random networks with 1,000 bridges as well as a realistic energy grid network within sub-seconds to seconds

The future roadmap of in-vehicle network processing: a HW-centric (R-)evolution

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The automotive industry is undergoing a deep revolution. With the race towards autonomous driving, the amount of technologies, sensors and actuators that need to be integrated in the vehicle increases exponentially. This imposes new great challenges in the vehicle electric/electronic (E/E) architecture and, especially, in the In-Vehicle Network (IVN). In this work, we analyze the evolution of IVNs, and focus on the main network processing platform integrated in them: the Gateway (GW). We derive the requirements of Network Processing Platforms that need to be fulfilled by future GW controllers focusing on two perspectives: functional requirements and structural requirements. Functional requirements refer to the functionalities that need to be delivered by these network processing platforms. Structural requirements refer to design aspects which ensure the feasibility, usability and future evolution of the design. By focusing on the Network Processing architecture, we review the available options in the state of the art, both in industry and academia. We evaluate the strengths and weaknesses of each architecture in terms of the coverage provided for the functional and structural requirements. In our analysis, we detect a gap in this area: there is currently no architecture fulfilling all the requirements of future automotive GW controllers. In light of the available network processing architectures and the current technology landscape, we identify Hardware (HW) accelerators and custom processor design as a key differentiation factor which boosts the devices performance. From our perspective, this points to a need - and a research opportunity - to explore network processing architectures with a strong HW focus, unleashing the potential of next-generation network processors and supporting the demanding requirements of future autonomous and connected vehicles.Peer ReviewedPostprint (published version

Time Sensitive Networking Protocol Implementation for Linux End Equipment

By bringing industrial-grade robustness and reliability to Ethernet, Time Sensitive Networking (TSN) offers an IEEE standard communication technology that enables interoperability between standard-conformant industrial devices from any vendor. It also eliminates the need for physical separation of critical and non-critical communication networks, which allows a direct exchange of data between operation centers and companies, a concept at the heart of the Industrial Internet of Things (IIoT). This article describes creating an end-to-end TSN network using specialized PCI Express (PCIe) cards and two final Linux endpoints. For this purpose, the two primary standards of TSN, IEEE 802.1AS (regarding clock synchronization), and IEEE 802.1Qbv (regarding time scheduled traffic) have been implemented in Linux equipment as well as a configuration and monitoring system.This work has been supported by the Ministerio de Economía y Competitividad of Spain within the project TEC2017-84011-R and FEDER funds as well as by the Department of Education of the Basque Government within the fund for research groups of the Basque university system IT978-16

A Survey of Scheduling in Time-Sensitive Networking (TSN)

TSN is an enhancement of Ethernet which provides various mechanisms for real-time communication. Time-triggered (TT) traffic represents periodic data streams with strict real-time requirements. Amongst others, TSN supports scheduled transmission of TT streams, i.e., the transmission of their packets by edge nodes is coordinated in such a way that none or very little queuing delay occurs in intermediate nodes. TSN supports multiple priority queues per egress port. The TAS uses so-called gates to explicitly allow and block these queues for transmission on a short periodic timescale. The TAS is utilized to protect scheduled traffic from other traffic to minimize its queuing delay. In this work, we consider scheduling in TSN which comprises the computation of periodic transmission instants at edge nodes and the periodic opening and closing of queue gates. In this paper, we first give a brief overview of TSN features and standards. We state the TSN scheduling problem and explain common extensions which also include optimization problems. We review scheduling and optimization methods that have been used in this context. Then, the contribution of currently available research work is surveyed. We extract and compile optimization objectives, solved problem instances, and evaluation results. Research domains are identified, and specific contributions are analyzed. Finally, we discuss potential research directions and open problems.Comment: 34 pages, 19 figures, 9 tables 110 reference

Time-Sensitive Networking for Industrial Automation: Challenges, Opportunities, and Directions

With the introduction of Cyber-Physical Systems (CPS) and Internet of Things (IoT) into industrial applications, industrial automation is undergoing tremendous change, especially with regard to improving efficiency and reducing the cost of products. Industrial automation applications are often required to transmit time- and safety-critical data to monitor and control industrial processes, especially for critical control systems. There are a number of solutions to meet these requirements (e.g., priority-based real-time schedules and closed-loop feedback control systems). However, due to their different processing capabilities (e.g., in the end devices and network switches), different vendors may come out with distinct solutions, and this makes the large-scale integration of devices from different vendors difficult or impossible. IEEE 802.1 Time-Sensitive Networking (TSN) is a standardization group formed to enhance and optimize the IEEE 802.1 network standards, especially for Ethernet-based networks. These solutions can be evolved and adapted into a cross-industry scenario, such as a large-scale distributed industrial plant, which requires multiple industrial entities working collaboratively. This paper provides a comprehensive review on the current advances in TSN standards for industrial automation. We present the state-of-the-art IEEE TSN standards and discuss the opportunities and challenges when integrating each protocol into the industry domains. Finally, we discuss some promising research about applying the TSN technology to industrial automation applications

Enabling Delegation of Control Plane Functionalities for Time Sensitive Networks

This paper proposes a new paradigm for control plane in Time Sensitive Networks (TSN). An SDN controller proactively instructs network elements on the reconfigurations to perform locally if some specific events occur (e.g., failures, performance degradations). Instructions are given in the form of Finite State Machines (FSMs), which store information related to the actions that each network element should execute to react against a specific event. Thus, if such event occurs, the SDN controller is by-passed reducing reaction (e.g., recovery) time. Such an approach is here implemented for recovery upon failures in TSN. Experiments of failure recovery are carried out and measurements are presented comparing the FSM-based solution with a fully-centralized reactive restoration. Moreover, the proposed approach is compared through simulations against Frame Replication and Elimination for Reliability. Results will show how proactive FSM manipulation can strongly reduce recovery time in SDN-based TSN networks without overloading the network with frame replicas

Real-Time Scheduling for Time-Sensitive Networking: A Systematic Review and Experimental Study

Time-Sensitive Networking (TSN) has been recognized as one of the key enabling technologies for Industry 4.0 and has been deployed in many time- and mission-critical industrial applications, e.g., automotive and aerospace systems. Given the stringent real-time communication requirements raised by these applications, the Time-Aware Shaper (TAS) draws special attention among the many traffic shapers developed for TSN, due to its ability to achieve deterministic latency guarantees. Extensive efforts on the designs of scheduling methods for TAS shapers have been reported in recent years to improve the system schedulability, each with their own distinct focuses and concerns. However, these scheduling methods have yet to be thoroughly evaluated, especially through experimental comparisons, to provide a systematical understanding on their performance using different evaluation metrics in various application scenarios. In this paper, we fill this gap by presenting a comprehensive experimental study on the existing TAS-based scheduling methods for TSN. We first categorize the system models employed in these work along with their formulated problems, and outline the fundamental considerations in the designs of TAS-based scheduling methods. We then perform extensive evaluation on 16 representative solutions and compare their performance under both synthetic scenarios and real-life industrial use cases. Through these experimental studies, we identify the limitations of individual scheduling methods and highlight several important findings. This work will provide foundational knowledge for the future studies on TSN real-time scheduling problems, and serve as the performance benchmarking for scheduling method development in TSN.Comment: 22 pages, ac

Using Machine Learning to Speed Up the Design Space Exploration of Ethernet TSN networks

In this work, we ask if Machine Learning (ML) can provide a viable alternative to conventional schedulability analysis to determine whether a real-time Ethernet network meets a set of timing constraints. Otherwise said, can an algorithm learn what makes it difficult for a system to be feasible and predict whether a configuration will be feasible without executing a schedulability analysis? In this study, we apply standard supervised and unsupervised ML techniques and compare them, in terms of their accuracy and running times, with precise and approximate schedulability analyses in Network-Calculus. We show that ML techniques are efficient at predicting the feasibility of realistic TSN networks and offer new trade-offs between accuracy and computation time especially interesting for design-space exploration algorithms

On the use of supervised machine learning for assessing schedulability: application to Ethernet TSN

In this work, we ask if Machine Learning (ML) can provide a viable alternative to conventional schedulability analysis to determine whether a real-time Ethernet network meets a set of timing constraints. Otherwise said, can an algorithm learn what makes it difficult for a system to be feasible and predict whether a configuration will be feasible without executing a schedulability analysis? To get insights into this question, we apply a standard supervised ML technique, k-nearest neighbors (k-NN), and compare its accuracy and running times against precise and approximate schedulability analyses developed in Network-Calculus. The experiments consider different TSN scheduling solutions based on priority levels combined for one of them with traffic shaping. The results obtained on an automotive network topology suggest that k-NN is efficient at predicting the feasibility of realistic TSN networks, with an accuracy ranging from 91.8% to 95.9% depending on the exact TSN scheduling mechanism and a speedup of 190 over schedulability analysis for 10^6 configurations. Unlike schedulability analysis, ML leads however to a certain rate "false positives'' (i.e., configurations deemed feasible while they are not). Nonetheless ML-based feasibility assessment techniques offer new trade-offs between accuracy and computation time that are especially interesting in contexts such as design-space exploration where false positives can be tolerated during the exploration process

A Comprehensive Review on Time Sensitive Networks with a Special Focus on Its Applicability to Industrial Smart and Distributed Measurement Systems

The groundbreaking transformations triggered by the Industry 4.0 paradigm have dramati-cally reshaped the requirements for control and communication systems within the factory systems of the future. The aforementioned technological revolution strongly affects industrial smart and distributed measurement systems as well, pointing to ever more integrated and intelligent equipment devoted to derive accurate measurements. Moreover, as factory automation uses ever wider and complex smart distributed measurement systems, the well-known Internet of Things (IoT) paradigm finds its viability also in the industrial context, namely Industrial IoT (IIoT). In this context, communication networks and protocols play a key role, directly impacting on the measurement accuracy, causality, reliability and safety. The requirements coming both from Industry 4.0 and the IIoT, such as the coexistence of time-sensitive and best effort traffic, the need for enhanced horizontal and vertical integration, and interoperability between Information Technology (IT) and Operational Technology (OT), fostered the development of enhanced communication subsystems. Indeed, established tech-nologies, such as Ethernet and Wi-Fi, widespread in the consumer and office fields, are intrinsically non-deterministic and unable to support critical traffic. In the last years, the IEEE 802.1 Working Group defined an extensive set of standards, comprehensively known as Time Sensitive Networking (TSN), aiming at reshaping the Ethernet standard to support for time-, mission-and safety-critical traffic. In this paper, a comprehensive overview of the TSN Working Group standardization activity is provided, while contextualizing TSN within the complex existing industrial technological panorama, particularly focusing on industrial distributed measurement systems. In particular, this paper has to be considered a technical review of the most important features of TSN, while underlining its applicability to the measurement field. Furthermore, the adoption of TSN within the Wi-Fi technology is addressed in the last part of the survey, since wireless communication represents an appealing opportunity in the industrial measurement context. In this respect, a test case is presented, to point out the need for wirelessly connected sensors networks. In particular, by reviewing some literature contributions it has been possible to show how wireless technologies offer the flexibility necessary to support advanced mobile IIoT applications