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arxiv: 1906.11596 · v1 · pith:ITWNNIEZnew · submitted 2019-06-27 · 💻 cs.NI

Reconfiguration Algorithms for High Precision Communications in Time Sensitive Networks: Time-Aware Shaper Configuration with IEEE 802.1Qcc (Extended Version)

Ahmed Nasrallah , Venkatraman Balasubramanian , Akhilesh Thyagaturu , Martin Reisslein , Hesham ElBakoury This is my paper

Pith reviewed 2026-05-25 14:05 UTC · model grok-4.3

classification 💻 cs.NI
keywords Time Sensitive NetworkingIEEE 802.1QccTime-Aware ShaperGate Control ListIndustrial Control NetworksDeterministic NetworkingScheduled TrafficTSN Configuration
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The pith

IEEE 802.1Qcc configuration of Time-Aware Shapers delivers ultra-low latency, zero loss, and minimal jitter for scheduled traffic on TSN networks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how the IEEE 802.1Qcc protocol can be used to set up transmission windows in Time-Aware Shaper switches. This setup allows scheduled traffic to meet strict timing requirements alongside regular traffic on Ethernet networks. The authors test both a hybrid centralized-distributed model and a fully distributed model. They aim to maximize the number of scheduled streams on a typical industrial control network while keeping latency low, loss at zero, and jitter minimal. If successful, this approach supports reliable operation for mission-critical applications like cyber-physical systems.

Core claim

The central claim is that accurate configuration of TAS-enabled switches using the IEEE 802.1Qcc management protocol ensures ultra-low latency, zero packet loss, and minimal jitter for scheduled TSN traffic. This holds in both the centralized network/distributed user (hybrid) model and the fully-distributed model when applied to a typical industrial control network, allowing maximization of scheduled traffic streams.

What carries the argument

The IEEE 802.1Qcc management protocol configuring Gate Control Lists (GCLs) with Gate Control Entries (GCEs) to set transmission windows in Time-Aware Shapers (TAS).

Load-bearing premise

The hybrid and fully-distributed 802.1Qcc models achieve the deterministic properties on a typical industrial control network without requiring post-hoc adjustments or additional unstated assumptions about the network.

What would settle it

A simulation or measurement showing that jitter exceeds minimal levels or packet loss occurs for scheduled traffic when using the 802.1Qcc configured GCLs in the hybrid or distributed model on the industrial control network.

Figures

Figures reproduced from arXiv: 1906.11596 by Ahmed Nasrallah, Akhilesh Thyagaturu, Hesham ElBakoury, Martin Reisslein, Venkatraman Balasubramanian.

Figure 1
Figure 1. Figure 1: The UNI provides a common method of requesting [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 1
Figure 1. Figure 1: Illustration of Centralized Network Configuration [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Our description focuses on the additions to the design [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 3
Figure 3. Figure 3: A TSN fully distributed configuration model example [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The main logical steps performed by each switch along [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Industrial control loop topology [20]. Each source [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
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Figure 6. Figure 6: Centralized Unidirectional Topology: Mean end-to-end [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
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Figure 8. Figure 8: Centralized Unidirectional Topology: Mean end-to-end [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
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Figure 9. Figure 9: Centralized Unidirectional Topology: Mean end-to-end [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
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Figure 10. Figure 10: Centralized Unidirectional Topology: Max delay as a [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗
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Figure 11. Figure 11: Centralized Unidirectional Topology: Stream Admis [PITH_FULL_IMAGE:figures/full_fig_p011_11.png] view at source ↗
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Figure 13. Figure 13: Centralized Unidirectional Topology: Stream average [PITH_FULL_IMAGE:figures/full_fig_p012_13.png] view at source ↗
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Figure 15. Figure 15: Centralized Unidirectional Topology: BE Total aver [PITH_FULL_IMAGE:figures/full_fig_p013_15.png] view at source ↗
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Figure 18. Figure 18: Centralized Bi-directional Topology: Mean end-to [PITH_FULL_IMAGE:figures/full_fig_p014_18.png] view at source ↗
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Figure 19. Figure 19: Centralized Bi-directional Topology: Mean end-to [PITH_FULL_IMAGE:figures/full_fig_p014_19.png] view at source ↗
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Figure 17. Figure 17: Centralized Unidirectional Topology: BE Frame loss [PITH_FULL_IMAGE:figures/full_fig_p014_17.png] view at source ↗
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Figure 20. Figure 20: Centralized Bi-directional Topology: Mean end-to [PITH_FULL_IMAGE:figures/full_fig_p014_20.png] view at source ↗
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Figure 21. Figure 21: Centralized Bi-directional Topology: Mean end-to [PITH_FULL_IMAGE:figures/full_fig_p015_21.png] view at source ↗
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Figure 22. Figure 22: Centralized Bi-directional Topology: Max delay as a [PITH_FULL_IMAGE:figures/full_fig_p015_22.png] view at source ↗
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Figure 23. Figure 23: Centralized Bi-directional Topology: Stream Admis [PITH_FULL_IMAGE:figures/full_fig_p016_23.png] view at source ↗
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Figure 25. Figure 25: Centralized Bi-directional Topology: Stream average [PITH_FULL_IMAGE:figures/full_fig_p017_25.png] view at source ↗
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Figure 27. Figure 27: Centralized Bi-directional Topology: BE Total average [PITH_FULL_IMAGE:figures/full_fig_p018_27.png] view at source ↗
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Figure 30. Figure 30: Decentralized Unidirectional Topology: Mean end-to [PITH_FULL_IMAGE:figures/full_fig_p019_30.png] view at source ↗
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Figure 31. Figure 31: Decentralized Unidirectional Topology: Mean end-to [PITH_FULL_IMAGE:figures/full_fig_p019_31.png] view at source ↗
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Figure 29. Figure 29: Centralized Bi-directional Topology: BE Frame loss [PITH_FULL_IMAGE:figures/full_fig_p019_29.png] view at source ↗
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Figure 32. Figure 32: Decentralized Unidirectional Topology: Mean end-to [PITH_FULL_IMAGE:figures/full_fig_p019_32.png] view at source ↗
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Figure 33. Figure 33: Decentralized Unidirectional Topology: Mean end-to [PITH_FULL_IMAGE:figures/full_fig_p020_33.png] view at source ↗
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Figure 34. Figure 34: Decentralized Unidirectional Topology: Max delay as [PITH_FULL_IMAGE:figures/full_fig_p020_34.png] view at source ↗
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Figure 35. Figure 35: Decentralized Unidirectional Topology: Stream ad [PITH_FULL_IMAGE:figures/full_fig_p021_35.png] view at source ↗
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Figure 37. Figure 37: Decentralized Unidirectional Topology: Stream Sig [PITH_FULL_IMAGE:figures/full_fig_p022_37.png] view at source ↗
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Figure 39. Figure 39: Decentralized Unidirectional Topology: BE Total [PITH_FULL_IMAGE:figures/full_fig_p023_39.png] view at source ↗
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Figure 42. Figure 42: Decentralized Bi-directional Topology: Mean end-to [PITH_FULL_IMAGE:figures/full_fig_p024_42.png] view at source ↗
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Figure 43. Figure 43: Decentralized Bi-directional Topology: Mean end-to [PITH_FULL_IMAGE:figures/full_fig_p024_43.png] view at source ↗
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Figure 41. Figure 41: Decentralized Unidirectional Topology: BE Frame [PITH_FULL_IMAGE:figures/full_fig_p024_41.png] view at source ↗
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Figure 44. Figure 44: Decentralized Bi-directional Topology: Mean end-to [PITH_FULL_IMAGE:figures/full_fig_p024_44.png] view at source ↗
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Figure 45. Figure 45: Decentralized Bi-directional Topology: Mean end-to [PITH_FULL_IMAGE:figures/full_fig_p025_45.png] view at source ↗
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Figure 46. Figure 46: Decentralized Bi-directional Topology: Max delay as [PITH_FULL_IMAGE:figures/full_fig_p025_46.png] view at source ↗
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Figure 47. Figure 47: Decentralized Bi-directional Topology: Stream admis [PITH_FULL_IMAGE:figures/full_fig_p026_47.png] view at source ↗
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Figure 50. Figure 50: Decentralized Bi-directional Topology: ST Total av [PITH_FULL_IMAGE:figures/full_fig_p027_50.png] view at source ↗
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Figure 51. Figure 51: Decentralized Bi-directional Topology: BE Total [PITH_FULL_IMAGE:figures/full_fig_p028_51.png] view at source ↗
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Figure 53. Figure 53: Decentralized Bi-directional Topology: BE Frame loss [PITH_FULL_IMAGE:figures/full_fig_p029_53.png] view at source ↗
read the original abstract

As new networking paradigms emerge for different networking applications, e.g., cyber-physical systems, and different services are handled under a converged data link technology, e.g., Ethernet, certain applications with mission critical traffic cannot coexist on the same physical networking infrastructure using traditional Ethernet packet-switched networking protocols. The IEEE 802.1Q Time Sensitive Networking (TSN) task group is developing protocol standards to provide deterministic properties on Ethernet based packet-switched networks. In particular, the IEEE 802.1Qcc, centralized management and control, and the IEEE 802.1Qbv, Time-Aware Shaper, can be used to manage and control scheduled traffic streams with periodic properties along with best-effort traffic on the same network infrastructure. In this paper, we investigate the effects of using the IEEE 802.1Qcc management protocol to accurately and precisely configure TAS enabled switches (with transmission windows governed by gate control lists (GCLs) with gate control entries (GCEs)) ensuring ultra-low latency, zero packet loss, and minimal jitter for scheduled TSN traffic. We examine both a centralized network/distributed user model (hybrid model) and a fully-distributed (decentralized) 802.1Qcc model on a typical industrial control network with the goal of maximizing scheduled traffic streams.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper investigates the use of the IEEE 802.1Qcc management protocol to configure Time-Aware Shaper (TAS) switches governed by Gate Control Lists (GCLs) in TSN networks. It examines both the hybrid (centralized network/distributed user) and fully-distributed 802.1Qcc models on a typical industrial control network, with the goal of maximizing the number of scheduled traffic streams while achieving ultra-low latency, zero packet loss, and minimal jitter for scheduled TSN traffic.

Significance. If the reconfiguration algorithms are shown to produce conflict-free GCLs that deliver the claimed deterministic properties under realistic conditions, the work would provide concrete guidance on applying 802.1Qcc and 802.1Qbv together for converged industrial networks, addressing a gap between TSN standards and deployable configurations.

major comments (2)
  1. [Abstract and evaluation description] The central claim that the hybrid and fully-distributed models ensure zero loss, ultra-low latency, and minimal jitter while maximizing streams rests on the unexamined assumptions that (a) the algorithms resolve all gate conflicts without residual interference and (b) the network model captures all relevant timing/queuing effects. No evidence is presented that clock drift, link variability, or non-ideal traffic patterns were included in the evaluation.
  2. [Abstract] The manuscript does not report quantitative results (e.g., measured latency distributions, loss rates, or jitter values) or the specific network topology/traffic parameters used, making it impossible to assess whether the stated deterministic properties were actually achieved or whether post-hoc adjustments were required.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract and evaluation description] The central claim that the hybrid and fully-distributed models ensure zero loss, ultra-low latency, and minimal jitter while maximizing streams rests on the unexamined assumptions that (a) the algorithms resolve all gate conflicts without residual interference and (b) the network model captures all relevant timing/queuing effects. No evidence is presented that clock drift, link variability, or non-ideal traffic patterns were included in the evaluation.

    Authors: The algorithms compute GCLs to eliminate transmission overlaps for scheduled streams under the periodic model of 802.1Qbv. The evaluation uses a simulator implementing the idealized TSN timing and queuing behavior. We agree that clock drift, link variability, and non-ideal patterns are not modeled and will add an explicit limitations discussion plus future-work directions on these effects in the revised manuscript. revision: yes

  2. Referee: [Abstract] The manuscript does not report quantitative results (e.g., measured latency distributions, loss rates, or jitter values) or the specific network topology/traffic parameters used, making it impossible to assess whether the stated deterministic properties were actually achieved or whether post-hoc adjustments were required.

    Authors: The abstract is a high-level summary. The full manuscript reports the quantitative results (latency, loss, jitter) and the industrial topology plus traffic parameters in the evaluation section. We will revise the abstract to include key quantitative findings and reference the topology/traffic parameters. revision: yes

Circularity Check

0 steps flagged

No circularity: investigative study of standards with no derivations or fitted predictions

full rationale

The paper investigates the application of IEEE 802.1Qcc models (hybrid and fully-distributed) to configure TAS/GCLs on an industrial network topology. No equations, parameter fits, or mathematical derivations are presented that could reduce to inputs by construction. The central claims rest on simulation/examination of standard-defined reconfiguration behaviors rather than any self-definitional loop, fitted-input prediction, or self-citation uniqueness theorem. This matches the default expectation for non-derivational papers; the reader's score of 2.0 is consistent with minor self-citation that is not load-bearing.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Relies on domain assumptions from the IEEE TSN task group that proper configuration yields deterministic properties; no free parameters, invented entities, or ad-hoc axioms introduced in the abstract.

axioms (1)
  • domain assumption IEEE 802.1Qcc and 802.1Qbv can be used to manage scheduled traffic streams with periodic properties along with best-effort traffic on the same network infrastructure.
    Stated as the foundation for the investigation in the abstract.

pith-pipeline@v0.9.0 · 5801 in / 1147 out tokens · 24075 ms · 2026-05-25T14:05:03.838200+00:00 · methodology

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Reference graph

Works this paper leans on

44 extracted references · 44 canonical work pages · 1 internal anchor

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