TSN Traffic Scheduling With Offline GCL Planning for Ultra-Low Latency
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Solution Overview
Problem
Existing TSN scheduling algorithms face challenges in practical implementation, particularly in industrial systems, with increased runtime and lack of feasibility due to limited evaluation of key components like controllers, sensors, and actuators, and few studies validate algorithms in real-world scenarios.
Innovation Solution
A Traffic Scheduling Method (TSM) is developed for TSN, involving network partitioning and traffic flow specification, with a Central Network Configurator (CNC) generating Gate Control Lists (GCL) and Central User Configurator (CUC) controlling message transmission, ensuring real-time performance by eliminating queuing delays and ultra-low latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If optimization theories such as SMT and ILP are applied to solve TSN scheduling problems, then scheduling accuracy is improved, but runtime increases dramatically
Solution Approach 1:
The patent segments the complex TSN scheduling problem into two distinct phases: (1) a planning phase that uses optimization theories (SMT/ILP) to generate an initial feasible schedule, and (2) a runtime phase that uses a lightweight event-driven scheduler to execute traffic flows according to pre-calculated time-aware shaper parameters. This segmentation allows the computationally intensive optimization to be performed offline, while runtime operations use simple lookup tables and timing mechanisms, thereby achieving both high scheduling accuracy and low runtime overhead.
Solution Approach 2:
The patent performs preliminary scheduling calculations offline using SMT or ILP optimization theories to determine the Gate Control Lists (GCLs) and time-aware shaper parameters before actual TSN traffic flows are established. These pre-calculated schedules are stored and reused during runtime, eliminating the need for complex real-time optimization computations. This preliminary action enables the system to achieve optimal scheduling accuracy without incurring high runtime costs during actual operations.
2Reliability
If TSN scheduling algorithms are implemented in real-world industrial systems, then practical feasibility is improved, but device complexity increases due to limited evaluation of key components
Solution Approach 1:
The patent implements a universal TSN scheduling framework that integrates multiple key industrial components (controllers, sensors, actuators, switches) into a cohesive system governed by centralized configuration servers. The time-aware shaper mechanism serves multiple functions simultaneously: it schedules different traffic classes (TS, NC, BE), manages bandwidth allocation, ensures real-time delivery guarantees, and coordinates across diverse device types. This multi-functional approach enables practical feasibility across various industrial applications while managing device complexity through standardized protocols and interfaces.
Solution Approach 2:
The patent introduces configuration servers as intermediary components that mediate between upper-layer applications and lower-layer TSN network devices. These servers handle the complex tasks of schedule calculation, GCL generation, and parameter configuration, thereby shielding end devices from complexity. The intermediaries translate high-level scheduling requirements into device-specific configuration parameters, enabling practical deployment in real-world industrial systems without requiring each device to handle complex scheduling logic independently.
3Manufacturing precision
If commercial ILP solvers are used to solve TSN scheduling formulations, then scheduling optimality is improved, but computational overhead increases
Solution Approach 1:
The patent segments the scheduling computation into offline planning (using ILP for optimality) and online execution (using lightweight lookup tables). The computationally intensive ILP-based optimization is performed offline to generate optimal GCLs and time-aware shaper parameters, while runtime operations simply retrieve and execute pre-calculated schedules. This segmentation achieves scheduling optimality through ILP without incurring high computational overhead during actual TSN traffic operations.
Solution Approach 2:
The patent performs preliminary schedule optimization using commercial ILP solvers offline before TSN traffic flows are established. The optimal schedules, including Gate Control Lists and time-aware shaper parameters, are pre-calculated and stored for runtime execution. This preliminary optimization action ensures scheduling optimality is achieved through rigorous mathematical optimization, while runtime computational overhead is minimized to simple table lookups and timing operations.
Data Source
AI summary
A time-sensitive network (TSN) standardized, maintained, and managed by the IEEE 802.1 Task Group improves the real-time and deterministic capabilities of Ethernet. However, traffic scheduling has not been standardized, and is being widely studied. Most research is essentially theoretical, and there are few practical verifications/research. In order to fill such a gap, the present invention provides detailed instructions for constructing an actual TSN-based process automation system first and regarding how a traffic scheduling method (TSM) will be distributed to industrial facilities. The present applicants empirically investigated the validity of a method according to the present invention and compared the performance thereof with that of a commercial TSN scheduler. The result shows that the method according to the present invention accurately schedules traffic such that robust real-time requirements are satisfied, and removes queuing delay, thereby accomplishing ultra-low latency.