Synthesis of Queue and Priority Assignment for Asynchronous Traffic Shaping in Switched Ethernet

Specht, Johannes; Samii, Soheil

doi:10.1109/rtss.2017.00024

Cited by 45 publications

(47 citation statements)

References 18 publications

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“…5) IEEE 802.1Qcr Asynchronous Traffic Shaping (ATS): While CQF and TAS provide ULL for critical traffic, they depend on network-wide coordinated time and, importantly, due to the enforced packet transmission at forced periodic cycles, they utilize network bandwidth inefficiently [89]. To overcome these shortcomings, the TSN TG has proposed the IEEE 802.1Qcr Asynchronous Traffic Shaper (ATS) [75], which is based on the urgency-based scheduler (UBS) [89], [93]. The ATS aims to smoothen traffic patterns by reshaping TSN streams per hop, implementing per-flow queues, and prioritizing urgent traffic over relaxed traffic.…”

Section: High Priority Trafficmentioning

confidence: 99%

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Nasrallah

Thyagaturu

Alharbi

et al. 2019

IEEE Commun. Surv. Tutorials

506

219

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Many network applications, e.g., industrial control, demand Ultra-Low Latency (ULL). However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds. The IEEE 802.1 Time Sensitive Networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF Deterministic Networking (DetNet) standards seek to provide the complementary network layer ULL support. This article provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies. The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity. ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks. We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management. Throughout, we identify the pitfalls and limitations of the existing standards and research studies. This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations.

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Section: High Priority Trafficmentioning

confidence: 99%

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Nasrallah

Thyagaturu

Alharbi

et al. 2019

IEEE Commun. Surv. Tutorials

506

219

View full text Add to dashboard Cite

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“…As mentioned in the previous subsection, the FP of the DetNet network consists of a set of TSN devices that implement an IEEE 802.1 Qcr ATS [4] at each of their egress ports. The ATS relies on the UBS proposed by Specht and Samii [5], [8]. In this work, we assume that the ATS follows the operation described in [5], [8].…”

Section: B Ats-based Forwarding Planementioning

confidence: 99%

“…The ATS relies on the UBS proposed by Specht and Samii [5], [8]. In this work, we assume that the ATS follows the operation described in [5], [8]. Then, an ATS consists of two levels queuing hierarchy (see Fig.…”

Section: B Ats-based Forwarding Planementioning

confidence: 99%

“…Several works address the performance guarantees analysis for an ATS-based network [5]- [7]. However, we believe that the flow allocation problem for ATS-based networks is only tackled in [8] by Specht and Samii. Specifically, they deal with the allocation of a set of flows, considering the path between every source-destination pair is predefined.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

LEARNET: Reinforcement Learning Based Flow Scheduling for Asynchronous Deterministic Networks

Prados-Garzon

Taleb

Bagaa

2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet) standards come to satisfy the needs of many industries for deterministic network services. That is the ability to establish a multi-hop path over an IP network for a given flow with deterministic Quality of Service (QoS) guarantees in terms of latency, jitter, packet loss, and reliability. In this work, we propose a reinforcement learning-based solution, which is dubbed LEARNET, for the flow scheduling in deterministic asynchronous networks. The solution leverages predictive data analytics and reinforcement learning to maximize the network operator's revenue. We evaluate the performance of LEARNET through simulation in a fifth-generation (5G) asynchronous deterministic backhaul network where incoming flows have characteristics similar to the four critical 5G QoS Identifiers (5QIs) defined in Third Generation Partnership Project (3GPP) TS 23.501 V16.1.0. Also, we compared the performance of LEARNET with a baseline solution that respects the 5QIs priorities for allocating the incoming flows. The obtained results show that, for the scenario considered, LEARNET achieves a gain in the revenue of up to 45% compared to the baseline solution.

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“…In order to achieve the flexibility of ATS by aggregating flows and assign separate priority level at each hop, the synthesis of ATS becomes a more complicated process, in terms of forwarding flows to queues and assigning priority levels to queues. In [17], Johannes and Soheil present a SMT based solution along with a topology rank, cluster based heuristic of this method. The work demonstrate that with the SMT method, it is feasible to find an existed solution of synthesis and the Topology Rank Solver (TRS) heuristic reduces the computational effort to achieve the method significantly.…”

Section: Relevant Work On Atsmentioning

confidence: 99%

Insight into the IEEE 802.1 Qcr Asynchronous Traffic Shaping in Time Sensitive Network

Zhou¹,

Berger²,

Ruepp³

et al. 2019

Adv. sci. technol. eng. syst. j.

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TSN is an attractive solution for latency-critical frame transmission built upon IEEE 802 architecture. Traffic scheduling and shaping in TSN aim to achieve bounded low latency and zero congestion loss. However, the most widespread solution (i.e. Time-Aware Shaper) requires a networkwide precision clock reference and only targets on cyclical traffic flows. This paper focuses on the performance evaluation of the ATS, which applies shaping algorithm to any flows and requires no clock reference. Simulations are proposed for evaluation and comparison. Metrics including end-to-end delay, buffer usage and frame loss rate are collected to assess the shaping performance. Results show that ATS achieves effective traffic shaping and switching without synchronous mechanisms, while there is an evident trade-off for using these specific algorithms.

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Synthesis of Queue and Priority Assignment for Asynchronous Traffic Shaping in Switched Ethernet

Cited by 45 publications

References 18 publications

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

LEARNET: Reinforcement Learning Based Flow Scheduling for Asynchronous Deterministic Networks

Insight into the IEEE 802.1 Qcr Asynchronous Traffic Shaping in Time Sensitive Network

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