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TSN Scheduling Algorithm for Real-Time Applications in a Heterogeneous Network

This repository documents the work presented in the Master's thesis "TSN Scheduling Algorithm for Real-Time Applications in a Heterogeneous Network", focused on the design and evaluation of scheduling algorithms for IEEE 802.1Qbv Time-Aware Shaper (TAS).


Overview

Time-Sensitive Networking (TSN) enables deterministic Ethernet communication for real-time applications with strict latency and jitter constraints. This work addresses the problem of scheduling time-sensitive (TS) flows in heterogeneous networks, where links differ in throughput, processing modes, and transmission characteristics.

The core contribution is a TAS scheduling framework that supports:

  • Heterogeneous network interfaces
  • Multi-period TS flows
  • Scheduling with and without data-plane reconfiguration
  • End-to-end delay and jitter optimization

The thesis was developed within the NextGeneration UNICO5G – TIMING project.


Problem Statement

Given a time-sensitive network with an existing scheduling plan:

  1. Scheduling without reconfiguration Accept a new TS request if feasible, without modifying the incumbent data plane.

  2. Scheduling with reconfiguration Accept a new TS request while producing a new scheduling plan that minimizes changes with respect to the incumbent one.

Challenges addressed include:

  • Heterogeneous throughput and processing modes (express vs store-and-forward)
  • Transmission modes (simplex, half-duplex, full-duplex)
  • Propagation delays and unavailable time slots
  • Pipeline dependencies between consecutive interfaces

IEEE 802.1Qbv Time-Aware Shaper (TAS)

The TAS divides time into repeating Super Frames (SF) composed of discrete time slots. For each network interface, the scheduler:

  • Discretizes the SF into atomic time slots
  • Assigns scheduling windows to TS flows
  • Guarantees guard bands and synchronization
  • Respects delay and jitter constraints
  • Solves a global optimization problem

Models

ILP Model

Two Integer Linear Programming (ILP) formulations are proposed:

  • Scheduling without reconfiguration Objective: minimize end-to-end jitter and delay.

  • Scheduling with reconfiguration Objective: minimize a weighted combination of:

    • Maximum delay
    • Maximum jitter
    • Number of data-plane changes

The models are inspired by the Job Shop Scheduling Problem (JSSP):

  • Network interfaces → machines
  • TS flow iterations → jobs
  • Scheduling windows → tasks

Heuristic Algorithm

To overcome ILP scalability limits, a heuristic approach is introduced:

  1. Constructive phase

    • Finds locally optimal starting time slots
    • Allocates scheduling windows
  2. Local Search (LS) phase

    • Jitter optimizer
    • Reconfiguration-aware dependency checker
    • Minimizes changes to the incumbent data plane

Additional optimizations include:

  • Pipeline scheduling inspired by microarchitecture pipelines
  • Circular shifting for multi-period flows
  • Space and time reduction techniques

Metrics

The scheduler estimates:

  • End-to-End Delay, composed of:

    • Signal delay
    • Propagation delay
    • Processing delay
    • Queuing / scheduling delay
  • End-to-End Jitter, defined as the difference between maximum and minimum delay across flow iterations


Experimental Results

Evaluation Setup

  • Comparison between:

    • ILP (solved with Gurobi)
    • Heuristic with jitter optimization
  • Fractional factorial Design of Experiments (DoE)

  • 3000 randomly generated TS flows

  • Simulations executed on an OpenStack VM (4 vCores, 32 GB RAM)

Key Findings

  • The heuristic achieves high feasibility ratios close to ILP
  • Significant reduction in execution time
  • Acceptable optimality gap
  • Effective support for heterogeneous wired and wireless interfaces

Contributions

  • Deterministic, reconfiguration-aware TAS scheduling
  • Support for heterogeneous network interfaces
  • Multi-period TS flow handling
  • Pipeline-based latency minimization
  • Practical applicability to provider transport networks

Applications

  • Industrial automation
  • Autonomous vehicles and ITS
  • Energy networks
  • Healthcare systems
  • Financial infrastructures

Related Publication

L. Velasco, G. Graziadei, Y. El Kaisi, J. Villares, O. Muñoz, J. Vidal, M. Ruiz Provisioning of Time-Sensitive and Non-Time-Sensitive Flows: from Control to Data Plane IFIP Networking 2024 – TENSOR Workshop

L. Velasco, G. Graziadei, S. Barzegar and M. Ruiz, "Provisioning of Time-Sensitive and Non-Time-Sensitive Flows With Assured Performance," in IEEE Transactions on Network and Service Management, vol. 22, no. 2, pp. 1484-1499, April 2025, doi: 10.1109/TNSM.2025.3539697.


Future Work

  • Validation with discrete-event simulators (ns-3, OMNeT++)
  • Guard band vs throughput trade-off analysis
  • Dynamic programming approaches
  • Support for dynamic TS flow requirements

License

This work is provided for academic and research purposes. Please cite appropriately if used in publications.

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