TSN Traffic Flow Modeling Using PLC Queue Feedback
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Solution Overview
Problem
Industrial networks face challenges in accurately predicting network performance due to varying traffic patterns and fluctuating loads, leading to inefficiencies and disruptions in time-sensitive networking (TSN) systems, where conventional metrics often result in excessive bandwidth wastage and conservative scheduling.
Innovation Solution
Implementing a lightweight firmware diagnostic based on Little's Formula that runs on programmable logic controllers (PLCs) to provide real-time adjustments and accurate network traffic flow modeling, utilizing hardware-assisted timestamps for precise queuing data analysis and controlling network parameters to optimize traffic flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional traffic load estimation methods are used, then network performance prediction is simplified, but accuracy of traffic load representation deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where actual network traffic measurements are continuously monitored and used to update and refine the traffic load model. This closed-loop approach allows the system to learn from real-world performance data and improve prediction accuracy over time, resolving the contradiction between simple estimation methods and accurate traffic representation.
Solution Approach 2:
The system dynamically adjusts traffic load parameters based on observed network conditions and actual device behavior. By changing parameters such as traffic patterns, bandwidth requirements, and timing characteristics to reflect real measurements rather than static estimates, the system achieves accurate traffic load representation without requiring complex manual configuration.
2Reliability
If static traffic specification (TSPEC) measures are used to cover worst case scenarios, then reliability of meeting deadlines is improved, but network efficiency deteriorates due to excessive guard banding
Solution Approach 1:
The patent transitions from static TSPEC measures to dynamic traffic specifications that adapt to actual network conditions. By continuously monitoring real traffic patterns and adjusting specifications accordingly, the system maintains reliable deadline guarantees while eliminating excessive guard banding, thus improving network efficiency without sacrificing reliability.
Solution Approach 2:
The system dynamically changes traffic specification parameters based on measured actual traffic behavior rather than using fixed worst-case estimates. This allows the network to allocate bandwidth more efficiently while still meeting time-sensitive requirements, resolving the contradiction between reliability and productivity.
3Measurement precision
If detailed component information is collected for accurate network modeling, then precision of bandwidth model is improved, but device complexity and data collection burden increase
Solution Approach 1:
The patent implements self-service mechanisms where network devices automatically generate and report their own traffic characteristics and performance data. This eliminates the need for manual data collection and complex configuration, while still providing accurate bandwidth modeling through autonomously collected information from actual device operation.
Solution Approach 2:
Instead of requiring detailed component information, the system creates accurate bandwidth models by copying and analyzing actual traffic patterns and performance data from the network. This empirical approach achieves precision without the complexity of detailed component specification and manual data gathering.
Data Source
AI summary
Systems and methods for accurate network traffic flow for time-sensitive networking in industrial systems. One configuration provides a system for controlling network traffic flow for time-sensitive networking in industrial systems. The system includes an electronic processor configured to receive, from an industrial controller of an industrial system, queuing data associated with a first queue of the industrial controller. The electronic processor is also configured to determine, based on the queuing data, a distribution of network traffic within the industrial system. The electronic processor is also configured to generate a network traffic model for display, the network traffic model including the distribution of network traffic within the industrial system.