143,656 research outputs found
A Basic Result on the Superposition of Arrival Processes in Deterministic Networks
Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet) are
emerging standards to enable deterministic, delay-critical communication in
such networks. This naturally (re-)calls attention to the network calculus
theory (NC), since a rich set of results for delay guarantee analysis have
already been developed there. One could anticipate an immediate adoption of
those existing network calculus results to TSN and DetNet. However, the
fundamental difference between the traffic specification adopted in TSN and
DetNet and those traffic models in NC makes this difficult, let alone that
there is a long-standing open challenge in NC. To address them, this paper
considers an arrival time function based max-plus NC traffic model. In
particular, for the former, the mapping between the TSN / DetNet and the NC
traffic model is proved. For the latter, the superposition property of the
arrival time function based NC traffic model is found and proved. Appealingly,
the proved superposition property shows a clear analogy with that of a
well-known counterpart traffic model in NC. These results help make an
important step towards the development of a system theory for delay guarantee
analysis of TSN / DetNet networks
TSN-Based Automotive E/E Architecture
Time-Sensitive Networking, also known as TSN, is a deterministic network based on traditional Ethernet. It offers a bunch of standards or profiles specified by IEEE 802.1 task group which has been evolved from the former IEEE802.1 Audio Video Bridging task group. In Automotive Industry, especially in ADAS domain, TSN backbone communication will gradually merge with or even replace the traditional in-vechile communication like CAN/CANFD/LIN/MOST/FlexRay due to below properties, it plays a key bridge role in heterogeneous SOC communication network
Scheduling in TSN networks using machine learning
The massive adoption of Ethernet technology in multiple sectors, produces the need to provide deterministic solutions to ensure a Quality of Service (QoS) that meets the requirements of time-triggered flows. For this, the Time-Sensitive Networking (TSN) Task Group (TG) of the IEEE 802.1 developed a set of standards that define mechanisms for time-sensitive transmissions of data over Ethernet networks. This project focuses on studying the feasibility of scheduling three classes of time-triggered flows with different time constraints over a simple network topology, which is made from two TSN (Time-Sensitive Networking) nodes connected through a link. Scheduling multiple time-triggered flows is a complex problem because the scheduling, if exists, must meet the time constraints of all these flows. To address this challenge, we explore the potential of using supervised machine learning classification models to accurately predict the feasibility of scheduling a given set of time-triggered flows, meeting their time-constraints, in a Time-Sensitive Network (TSN). Supervised models require a training dataset that contains a data matrix and a class label vector. To obtain the class label vector of each observation, we use an adaptation of the implementation developed in [27] of the Integer Linear Programming (ILP) model introduced in [33]. Two different models are considered: K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM). These algorithms are tested and built from the application of the Leave One Out Cross-Validation (LOOCV) technique with the generated datasets, and the results obtained are compared and discussed. Finally, a hybrid verification strategy is proposed to train and test machine learning models, drastically reducing the resources and computation time originally required to compute the class label of each observation of the dataset
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
Software-Defined Networks Supporting Time-Sensitive In-Vehicular Communication
Future in-vehicular networks will be based on Ethernet. The IEEE
Time-Sensitive Networking (TSN) is a promising candidate to satisfy real-time
requirements in future car communication. Software-Defined Networking (SDN)
extends the Ethernet control plane with a programming option that can add much
value to the resilience, security, and adaptivity of the automotive
environment. In this work, we derive a first concept for combining
Software-Defined Networking with Time-Sensitive Networking along with an
initial evaluation. Our measurements are performed via a simulation that
investigates whether an SDN architecture is suitable for time-critical
applications in the car. Our findings indicate that the control overhead of SDN
can be added without a delay penalty for the TSN traffic when protocols are
mapped properly.Comment: To be published at IEEE VTC2019-Sprin
- …