Network Fabric Simulation Stability Validation
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Solution Overview
Problem
Current network evaluation techniques lack effective methods for deriving accurate network configurations and states for simulating physical network fabrics, particularly in determining stability and generating stable or unstable conditions, and merging configurations from multiple topologies.
Innovation Solution
The system collects configuration and environmental data from network devices, validates network stability, generates stability scores, and classifies configurations as stable or unstable, storing data records that include version information, stability scores, and failure events. It also simulates network configurations based on target parameters and extrapolates new configurations from existing ones for simulating stable, unstable, or error conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network configuration data is collected and validated from multiple devices and topologies, then simulation accuracy is improved, but data processing complexity and time increase
Solution Approach 1:
The system segments the network configuration validation process into distinct modules: data collection from devices, stability validation, configuration merging, and simulation generation. Each module handles specific aspects independently, reducing overall processing complexity while maintaining comprehensive validation accuracy.
Solution Approach 2:
The system performs preliminary stability validation and configuration verification before merging data from multiple topologies. By pre-validating individual device configurations and identifying stable states beforehand, the system reduces the computational burden during the merging phase and ensures higher simulation accuracy without proportionally increasing processing complexity.
2Reliability
If stability validation and scoring are performed on network configurations, then configuration reliability is improved, but processing time increases
Solution Approach 1:
The system transforms qualitative stability assessments into quantitative stability scores with specific thresholds. By defining numerical parameters for stability validation (e.g., score thresholds for stable/unstable classifications), the system automates the validation process, improving configuration reliability through consistent criteria while reducing manual processing time.
Solution Approach 2:
The system implements feedback mechanisms where stability validation results from previous configurations inform subsequent validation processes. By learning from historical stability data and adjusting validation parameters accordingly, the system improves configuration reliability over time while optimizing processing efficiency through experience-based adjustments.
3Measurement precision
If network configurations are simulated based on validated data, then evaluation accuracy is improved, but computational resources increase
Solution Approach 1:
The system performs simulation on a subset of validated stable configurations rather than all possible network states. By selectively simulating only those configurations that meet stability criteria and represent typical operational scenarios, the system achieves sufficient evaluation accuracy while significantly reducing computational resource requirements compared to exhaustive simulation approaches.
Data Source
AI summary
Systems and methods are described for collecting configuration data associated with one or more devices of a network, in association with a configuration of the network. The systems and methods include validating the configuration of the network. Validating the configuration includes determining a stability status associated with the network and the configuration. The systems and methods include generating a data record corresponding to the configuration of the network and storing the data record to a data repository. The data record includes the configuration data and results associated with validating the configuration of the network. The systems and methods include generating a second configuration and simulating the second network based on the second configuration. The second configuration includes the one or more devices, one or more second devices included in the data repository, or both.