Clustered Network Emulation for Lower-Complexity Digital Twins
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Solution Overview
Problem
As networks grow in size and complexity, executing accurate and efficient network emulations becomes increasingly challenging due to the computational and resource-intensive nature of traditional one-to-one mappings between physical and digital counterparts.
Innovation Solution
The technique involves clustering network devices based on configuration data representations to generate device models, forming device clusters, and creating a digital twin network using these models, which reduces the number of models needed for emulation, thereby decreasing computational complexity and storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional one-to-one mapping between physical and digital network counterparts is used, then network emulation accuracy is maintained, but computational complexity and resource requirements increase significantly
Solution Approach 1:
The patent merges multiple physical network devices into a single digital twin by clustering devices with similar configuration characteristics. Instead of creating separate digital representations for each device, the system identifies commonalities in device configurations and consolidates them into unified digital models, thereby reducing computational complexity while preserving essential network behavior patterns.
Solution Approach 2:
The patent creates universal digital twin templates that can represent multiple different physical devices. These templates capture common configuration patterns and behaviors that apply across device types, allowing a single digital model to serve multiple physical counterparts. This multi-functional approach reduces the total number of digital twins needed while maintaining emulation accuracy for clustered devices.
2Loss of information
If traditional one-to-one mapping between physical and digital network counterparts is used, then detailed network representation is achieved, but storage requirements increase significantly
Solution Approach 1:
The patent combines configuration data from multiple physical devices into consolidated digital twin representations. By identifying and merging common configuration elements across devices, the system reduces redundant data storage while preserving the essential network topology and device behavior information needed for accurate emulation.
Solution Approach 2:
The patent transforms detailed device-specific configuration parameters into generalized cluster-level parameters. By abstracting common configuration characteristics into shared digital twin templates, the system reduces the total volume of stored configuration data while maintaining the ability to represent network behavior accurately through parameterized models.
3Productivity
If clustering of network devices is implemented, then computational efficiency improves, but emulation accuracy may be reduced
Solution Approach 1:
The patent applies local quality by creating different levels of digital twin abstraction. Highly critical or unique devices maintain detailed one-to-one digital representations, while groups of similar devices are represented by clustered templates. This differentiated approach ensures that emulation accuracy is maintained where needed while achieving computational efficiency through clustering in appropriate scenarios.
Solution Approach 2:
The patent implements dynamic clustering where the granularity of device grouping can be adjusted based on emulation requirements. The system can dynamically determine the appropriate level of clustering versus individual device representation based on factors such as device criticality, configuration uniqueness, and emulation objectives, allowing flexibility in balancing accuracy and efficiency.
Data Source
AI summary
This disclosure describes techniques for emulating the operations of a computer network. In some cases, a method includes receiving first configuration data associated with a first device and second configuration data associated with a second device in a network; determining a first device representation associated with the first device based on the first configuration data and a second device representation associated with the second device based on the second configuration data; determining, based on the first and the second device representations, a first device cluster, wherein the first device cluster comprises a plurality of clustered devices comprising the first device and the second device; determining a first device model associated with the first device cluster, wherein the first device model represents a first device behavior that is common across the plurality of clustered devices; and generating, based on the first device model, an emulation environment associated with the network.