Network Digital Twin Using Probabilistic Traffic Flow Modeling
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Solution Overview
Problem
Creating a digital twin of a network that accurately reflects the interplay between access networks, user home networks, devices, and services is challenging, particularly for assessing capacity and bandwidth requirements and optimizing configurations.
Innovation Solution
A digital twin system that selects parameters from probabilistic distributions for network configurations and service executions, determines data volume injection sequences, and outputs data flow information, replicating network behaviors by considering various network elements and user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a digital twin is created to accurately represent network behavior and interplay between access network, home network, devices and services, then the accuracy and realism of the digital representation is improved, but the complexity of the system and difficulty of creating such a representation increases
Solution Approach 1:
The digital twin system segments the network into distinct components: access network elements, home network elements, end devices, and services. Each component is modeled separately with its own parameters and behaviors, then integrated to create the overall digital twin. This segmentation reduces the complexity of creating an accurate representation by breaking down the complex network into manageable parts.
Solution Approach 2:
The system uses probabilistic distributions to represent parameters of network configurations and service executions. Instead of requiring exact values for all parameters, the system models them as probability distributions, which simplifies the digital twin creation while maintaining accuracy in representing network behavior and interplay between different components.
2Measurement precision
If detailed user input is required for network technologies and user behaviors to create an accurate digital twin, then the accuracy of the digital representation is improved, but the ease of operation and setup time increases
Solution Approach 1:
The digital twin system automatically generates network configurations and service executions using probabilistic distributions. Instead of requiring detailed user input for each parameter, the system self-configures by sampling from predefined probability distributions that capture typical network behaviors and interplay patterns, thereby maintaining accuracy while significantly improving ease of setup.
Solution Approach 2:
The system pre-defines probabilistic distributions for network configuration parameters and service execution parameters based on observed network behaviors. These distributions are prepared in advance, allowing the digital twin to be quickly instantiated without requiring users to provide detailed input for each parameter, thus improving ease of operation while maintaining representational accuracy.
Data Source
AI summary
A system is configured to perform selecting one or more first values representing respective parameters of a configuration; selecting one or more second values representing respective parameters of a service to be executed on one or more end devices of the network, each service associated with transmission of one or more data packets through the network; determining, based on the one or more selected second values, a time sequence representing a volume of data to be injected into the network at each of one or more time intervals; determining, based on the one or more selected first values and the time sequence, a flow of the volume of data through the network; and outputting information indicative of the flow of the volume of data.