Network Calculus Results for TSN: An Introduction

Maile, Lisa; Hielscher, Kai-Steffen; German, Reinhard

doi:10.1109/ictc49638.2020.9123308

Cited by 39 publications

(28 citation statements)

References 28 publications

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“…One of the key ingredients to provide deterministic QoS is to implement mathematically analyzable queuing algorithms for handling the frames at the output ports of the FP bridges [1]. In other words, we can derive analytical expressions for the end-to-end (E2E) maximum delay, jitter, and frame loss, experienced by any critical flow given its characteristics, the current state of the network, and the resources allocated to it [20]. Then, the flow allocation algorithms will rely on these expressions to decide in advance the setup and resources to be reserved for every flow in order to ensure its QoS requirements.…”

Section: B Asynchronous Tsn Networkmentioning

confidence: 99%

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Optimization of Flow Allocation in Asynchronous Deterministic 5G Transport Networks by Leveraging Data Analytics

Prados-Garzon¹,

Taleb²,

Bagaa³

2021

IEEE Trans. on Mobile Comput.

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Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet) technologies are increasingly recognized as key levers of the future 5G transport networks (TNs) due to their capabilities for providing deterministic Quality-of-Service and enabling the coexistence of critical and best-effort services. Additionally, they rely on programmable and cost-effective Ethernetbased forwarding planes. This article addresses the flow allocation problem in 5G backhaul networks realized as asynchronous TSN networks, whose building block is the Asynchronous Traffic Shaper. We propose an offline solution, dubbed "Next Generation Transport Network Optimizer" (NEPTUNO), that combines exact optimization methods and heuristic techniques and leverages data analytics to solve the flow allocation problem. NEPTUNO aims to maximize the flow acceptance ratio while guaranteeing the deterministic Quality-of-Service requirements of the critical flows. We carried out a performance evaluation of NEPTUNO regarding the degree of optimality, execution time, and flow rejection ratio. Furthermore, we compare NEPTUNO with a novel online baseline solution for two different optimization goals. Online methods compute the flow's allocation configuration right after the flow arrives at the network, whereas offline solutions like NEPTUNO compute a long-term configuration allocation for the whole network. Our results highlight the potential of data analytics for the self-optimization of the future 5G TNs.

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Section: B Asynchronous Tsn Networkmentioning

confidence: 99%

“…Last, the optimization of the transit hops is performed by solving another MILP whose objective is given by (20). Furthermore, this MILP includes additional constraints compared to that for the last hops in order to ensure the QAR2 rule in the subsequent hops.…”

Section: A Operationmentioning

confidence: 99%

Optimization of Flow Allocation in Asynchronous Deterministic 5G Transport Networks by Leveraging Data Analytics

Prados-Garzon¹,

Taleb²,

Bagaa³

2021

IEEE Trans. on Mobile Comput.

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“…Thus, in each hyper-period, there is an contention-free intervals ( = ℎ ℎ ⁄ ). By considering the th cycle as a benchmark, the lower bound of the service curve can be given as [6], The term ̅ ℎ, is found using ( 29) and ( 11) and ℎ, using ( 8) and (10). Note that , ℎ ( ) is expressed for the TDMA protocol [6] as,…”

Section: ) Lower-bound Service Curve " ( )" Determinationmentioning

confidence: 99%

“…Thus, analytical models are pivotal in safetycritical real-time systems. One of the preferred and trusted analytical approaches in real-time systems is the Network Calculus (NC) [9] as it produces safe and less pessimistic latency evaluations [10], [11]. For this reason, the NC approach is adopted here to formulate the proposed model.…”

Section: Introductionmentioning

confidence: 99%

Critical Offset Optimizations for Overlapping-Based Time-Triggered Windows in Time-Sensitive Network

et al. 2021

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Deterministic and low latency communications are increasingly becoming essential requirements for several safety-critical applications, such as automotive and automation industries. The timesensitive networking (TSN) is an element of the new IEEE 802.1 standards that introduced Ethernet-based amendments to support these applications. One of these enhancements was presented in IEEE 802.1Qbv to define time-aware shaping (TAS) technique for time-triggered (TT) traffic scheduling. The TAS mechanism is a window-based scheduling using a gating system controlled by the gate control list (GCL) schedules in all nodes. Although several scheduling algorithms have been proposed to investigate the effects of windowrelated parameters on network performance, the offset difference ( ) between the same-class windows in the adjoining nodes has not been optimized yet. These optimizations are extremely crucial to implement less pessimistic latency schedules. In this paper, we propose an optimized flexible window-overlapping scheduling (OFWOS) algorithm that optimizes TT window offsets based on latency evaluations considering the overlapping between different priority windows at the same node. First, we formulate the GCL timings as mathematical forms under variable s between the same-priority windows. Then, an analytical model is implemented using the network calculus (NC) approach to express the worst-case end-to-end delay ( ) for TT flows and evaluated using a realistic vehicular use case. The OFWOS model optimizes under all overlapping situations between TT windows at the same node. In comparison with latest works of 3-hop and 30-hop TSN connections, the OFWOS reduces the bounds by 8.4% and 32.6%, respectively which accomplishes less pessimistic end-to-end latency bounds.

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“…Researchers [37] have given a comprehensive survey on TSN networks, from TSN sub-standards to the existing research of TSN before 2018. Maile et al [38] provide an overview of the exsiting publications that use a Network Calculus approach in the timing analysis for TSN networks. Researchers in [19] make a comparison between flow-based (i.e., frame-based) and class-based TAS, which concludes that class-based scheduling is easy to plan but loses the advantages of extremely low latency and jitter compared with the flowbased TAS.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao,

Pop,

Steinhorst

2021

Preprint

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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.

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Network Calculus Results for TSN: An Introduction

Cited by 39 publications

References 28 publications

Optimization of Flow Allocation in Asynchronous Deterministic 5G Transport Networks by Leveraging Data Analytics

Optimization of Flow Allocation in Asynchronous Deterministic 5G Transport Networks by Leveraging Data Analytics

Critical Offset Optimizations for Overlapping-Based Time-Triggered Windows in Time-Sensitive Network

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

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