Network Device Modeling via Automated Configuration
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Solution Overview
Problem
Manual configuration of test beds for network devices is time-consuming and often diverges from real-world network configurations, making it difficult to create realistic models for device testing.
Innovation Solution
An automated driver randomizes configuration settings to reflect real-world networks, iteratively configuring both the device under test and the test bed, and conducts simulations to collect operational data, which is then used to generate predictive models using machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual configuration of test bed is used, then configuration can be precisely controlled, but it is time-consuming and diverges from real-world network configurations
Solution Approach 1:
The patent uses template-based configuration where pre-defined templates represent real-world network configurations. Instead of manually creating each configuration from scratch, the system copies and adapts these templates to generate test bed configurations that closely mirror actual production environments, thereby reducing configuration time while maintaining precision.
Solution Approach 2:
The system employs automated configuration generation that self-adjusts parameters based on template definitions and device specifications. The automated processes handle configuration creation, validation, and adjustment without requiring continuous manual intervention, significantly reducing configuration time while maintaining consistency with real-world networks.
2Manufacturing precision
If manual configuration of test bed is used, then configuration can be precisely controlled, but the resulting configuration diverges from real-world networks
Solution Approach 1:
The patent systematically varies configuration parameters within defined ranges based on real-world network characteristics. Instead of using fixed manual configurations, the system dynamically adjusts parameters such as network topology, device roles, and service configurations to reflect the variability found in actual production networks, thereby improving model realism while maintaining configuration precision through controlled parameter spaces.
Solution Approach 2:
The configuration system transitions from static manual configurations to dynamic template-based generation. The templates incorporate variability and adaptability, allowing the test bed configurations to dynamically reflect different real-world scenarios. This dynamic approach ensures that configurations remain realistic and representative of actual network environments while maintaining precise control over configuration parameters.
3Measurement precision
If automated driver randomizes configuration settings, then model accuracy improves, but configuration complexity increases
Solution Approach 1:
The patent segments the configuration process into distinct components: templates define the structure, parameters define the variability, and the automated driver orchestrates their combination. This segmentation allows the system to manage complexity by handling each aspect separately while maintaining overall model accuracy through the coordinated interaction of these modular components.
Solution Approach 2:
The automated driver acts as an intermediary layer between the configuration templates and the actual test bed deployment. It manages the complexity of randomization and parameter adjustment by providing a controlled interface that translates high-level template definitions into detailed device configurations, thereby maintaining model accuracy while abstracting away the complexity from manual operations.
Data Source
AI summary
In general, techniques are described for providing network device modeling in preconfigured network modeling environments. A device comprising a memory and a processor may be configured to perform the techniques. The processor may interface with a network device within the preconfigured network environment to iteratively adapt pre-defined configuration objects of the network device. The processor may conduct, for each iteration of the adaptation of the pre-defined configuration objects, a simulation to collect a simulation dataset representative of an operating state of the network device. The processor may generate, based on the operational data, a model representative of the network device that predicts, responsive to configuration parameters for the network device, an operating state of the network device. The memory may store the model.