Improved Delay Bound for a Service Curve Element with Known Transmission Rate

Network calculus is often used to prove delay bounds in deterministic networks, using arrival and service curves. We consider a FIFO system that offers a rate-latency service curve and where packet transmission occurs at line rate without pre-emption. The existing network calculus delay bounds take advantage of the service curve guarantee but not of the fact that transmission occurs at full line rate. In this letter, we provide a novel, improved delay bound which takes advantage of these two features. Contrary to existing bounds, ours is per-packet and depends on the packet length. We prove that it is tight.Comment: 4 pages, 2 figure

On the Robustness of Deep Learning-predicted Contention Models for Network Calculus

The network calculus (NC) analysis takes a simple model consisting of a network of schedulers and data flows crossing them. A number of analysis "building blocks" can then be applied to capture the model without imposing pessimistic assumptions like self-contention on tandems of servers. Yet, adding pessimism cannot always be avoided. To compute the best bound on a single flow's end-to-end delay thus boils down to finding the least pessimistic contention models for all tandems of schedulers in the network - and an exhaustive search can easily become a very resource intensive task. The literature proposes a promising solution to this dilemma: a heuristic making use of machine learning (ML) predictions inside the NC analysis. While results of this work were promising in terms of delay bound quality and computational effort, there is little to no insight on when a prediction is made or if the trained algorithm can achieve similarly striking results in networks vastly differing from its training data. In this paper, we address these pending questions. We evaluate the influence of the training data and its features on accuracy, impact and scalability. Additionally, we contribute an extension of the method by predicting the best $n$ contention model alternatives in order to achieve increased robustness for its application outside the training data. Our numerical evaluation shows that good accuracy can still be achieved on large networks although we restrict the training to networks that are two orders of magnitude smaller

Routing Optimization of AVB Streams in TSN Networks

In this paper we are interested in safety-critical real-time applications implemented on distributed architectures using the Time-Sensitive Networking (TSN) standard. The ongoing standardization of TSN is an IEEE effort to bring deterministic real-time capabilities into the IEEE 802.1 Ethernet standard supporting safety-critical systems and guaranteed Quality-of-Service. TSN will support Time-Triggered (TT) communication based on schedule tables, Audio-Video-Bridging (AVB) streams with bounded end-to-end latency as well as Best-Effort messages. We consider that we know the topology of the network as well as the routes and schedules of the TT streams. We are interested to determine the routing of the AVB streams such that all frames are schedulable and their worst-case end-to-end delay is minimized. We have proposed a search-space reduction technique and a Greedy Randomized Adaptive Search Procedure (GRASP)-based heuristic for this routing optimization problem. The proposed approaches has been evaluated using several test cases. </jats:p

Impact on credit freeze before gate closing in CBS and GCL integration into TSN

International audienceThe Time Sensitive Networking (TSN) task group has added a set of mechanisms to Ethernet in order to provide a real-time network. In particular, the output port scheduling based on a Credit-Based Shaper (CBS) algorithm, that was introduced formerly by the Audio-Video Bridging (AVB) task group, has been enhanced with a time driven Gate Control List (GCL). This implies some update in the credit evolution rules, and several solutions may exist. In this paper, we compare the solution used in the standard with another one used in most papers, and also with a third one, designed as a trade-off between the two others. The comparison is first done on some hand-made examples, showing some credit overflow and unfairness potential problems. Then, simulations are done on a single switch with 3 CBS queues

Latency Analysis of Multiple Classes of AVB Traffic in TSN with Standard Credit Behavior using Network Calculus

Time-Sensitive Networking (TSN) is a set of amendments that extend Ethernet to support distributed safety-critical and real-time applications in the industrial automation, aerospace and automotive areas. TSN integrates multiple traffic types and supports interactions in several combinations. In this paper we consider the configuration supporting Scheduled Traffic (ST) traffic scheduled based on Gate-Control-Lists (GCLs), Audio-Video-Bridging (AVB) traffic according to IEEE 802.1BA that has bounded latencies, and Best-Effort (BE) traffic, for which no guarantees are provided. The paper extends the timing analysis method to multiple AVB classes and proofs the credit bounds for multiple classes of AVB traffic, respectively under frozen and non-frozen behaviors of credit during guard band (GB). They are prerequisites for non-overflow credits of Credit-Based Shaper (CBS) and preventing starvation of AVB traffic. Moreover, this paper proposes an improved timing analysis method reducing the pessimism for the worst-case end-to-end delays of AVB traffic by considering the limitations from the physical link rate and the output of CBS. Finally, we evaluate the improved analysis method on both synthetic and real-world test cases, showing the significant reduction of pessimism on latency bounds compared to related work, and presenting the correctness validation compared with simulation results. We also compare the AVB latency bounds in the case of frozen and non-frozen credit during GB. Additionally, we evaluate the scalability of our method with variation of the load of ST flows and of the bandwidth reservation for AVB traffic

Insights on the Performance and Configuration of AVB and TSN in Automotive Ethernet Networks

Switched Ethernet is profoundly reshaping in-car communications. To meet the diverse real-time requirements in automotive communications, Quality-of-Service protocols that go beyond the mere use of priorities are required. In this work, the basic questions that we investigate on a case-study with diverse and demanding communication requirements is what can we expect from the various protocols aimed at providing a better timing Quality of Service on top of Ethernet? And how to use them? Especially how to use them in a combined manner. We will focus on the Credit-Based Shaper of AVB, the Time-Aware Shaper of TSN and the use of priorities as defined in IEEE802.1Q. The performance metrics considered are the distributions of the communication latencies, obtained by simulation, as well as upper bounds on these quantities obtained by worst-case schedulability analysis. If there have been over the last 5 years numerous studies on the performance of AVB CBS, the literature on comparing AVB to TSN and other candidate protocols is still sparse. To the best of our knowledge, this empirical study is the first to consider most protocols currently considered in the automotive domain, with the aim to gain insights into the different technological, design and configurations alternatives. In particular, an objective of this study is to identify key problems that need to be solved in order to further automate network design and configuration

Quantitative Performance Comparison of Various Traffic Shapers in Time-Sensitive Networking

Owning to the sub-standards being developed by IEEE Time-Sensitive Networking (TSN) Task Group, the traditional IEEE 802.1 Ethernet is enhanced to support real-time dependable communications for future time- and safety-critical applications. Several sub-standards have been recently proposed that introduce various traffic shapers (e.g., Time-Aware Shaper (TAS), Asynchronous Traffic Shaper (ATS), Credit-Based Shaper (CBS), Strict Priority (SP)) for flow control mechanisms of queuing and scheduling, targeting different application requirements. These shapers can be used in isolation or in combination and there is limited work that analyzes, evaluates and compares their performance, which makes it challenging for end-users to choose the right combination for their applications. This paper aims at (i) quantitatively comparing various traffic shapers and their combinations, (ii) summarizing, classifying and extending the architectures of individual and combined traffic shapers and their Network calculus (NC)-based performance analysis methods and (iii) filling the gap in the timing analysis research on handling two novel hybrid architectures of combined traffic shapers, i.e., TAS+ATS+SP and TAS+ATS+CBS. A large number of experiments, using both synthetic and realistic test cases, are carried out for quantitative performance comparisons of various individual and combined traffic shapers, from the perspective of upper bounds of delay, backlog and jitter. To the best of our knowledge, we are the first to quantitatively compare the performance of the main traffic shapers in TSN. The paper aims at supporting the researchers and practitioners in the selection of suitable TSN sub-protocols for their use cases

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