Telecommunications Network Model Reduction for Faster Failure Simulation
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Solution Overview
Problem
Conventional methods for modeling and simulating telecommunications networks are computationally burdensome and time-consuming due to the complexity of large networks with thousands of components and millions of transmission paths, requiring hours or days to complete simulations.
Innovation Solution
A method involving generating an initial network model, applying transformations to combine similar nodes and edges, and reducing the number of nodes and edges, followed by simulating failure scenarios using routing schemes like ECMP, shortest path, and Label-Switched Paths to optimize computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional simulation techniques are used to model large telecommunications networks, then simulation accuracy is maintained, but computational time and resources become excessively high
Solution Approach 1:
The patent divides the large telecommunications network into multiple sub-networks or regions, modeling each segment separately with appropriate detail level. This segmentation allows the system to maintain accuracy for critical components while using simplified models for less critical areas, dramatically reducing overall computational time while preserving simulation accuracy where needed.
Solution Approach 2:
The patent applies different levels of modeling detail to different parts of the network based on their importance and complexity. Critical network components are modeled with high detail and accuracy, while non-critical components use simplified models. This local quality approach ensures simulation accuracy is concentrated where most needed while reducing overall computational burden.
2Measurement precision
If detailed modeling of each network component and transmission path is performed, then simulation accuracy is improved, but device complexity and computational burden increase
Solution Approach 1:
The patent segments the network model into hierarchical levels, with detailed models for critical components and aggregated models for less critical areas. This segmentation reduces model complexity by eliminating the need to represent every single component with equal detail, while maintaining accuracy for the most important network elements.
Solution Approach 2:
The patent combines multiple similar network components into aggregated representations when they share common characteristics and behavior. This merging reduces model complexity by representing groups of components with single equivalent models, while still capturing the essential behavior and performance characteristics of the underlying detailed system.
3Reliability
If the entire network is simulated in detail, then comprehensive results are obtained, but productivity and efficiency of the modeling process decrease
Solution Approach 1:
The patent divides the comprehensive network analysis into multiple manageable segments that can be modeled and simulated independently. This segmentation enables parallel processing and faster computation while maintaining comprehensive coverage of the entire network. Critical segments are analyzed in detail while less critical segments use simplified models, improving overall productivity.
Solution Approach 2:
The patent performs preliminary analysis to identify which network segments and components require detailed modeling versus simplified modeling. By pre-classifying network elements based on their importance and impact, the system can allocate computational resources efficiently, performing comprehensive analysis only where necessary and using simplified models elsewhere, thereby improving productivity without sacrificing reliability.
Data Source
AI summary
Aspects of the present disclosure include systems, methods, computing devices, computer-implemented methods, and the like for modeling and/or simulating performance of a telecommunications network during one or more failure scenarios that reduces computational time and/or power over previous simulation techniques. Modeling and simulating the network may include generating an initial network model from network data information and applying one or more transformations to the initial network model to reduce the size of the model. Following transformation, simulation methods may be applied to the generated network model based on routing characteristics of the components of the network. To reduce the computations utilized to simulate such components and/or routing decisions in the network, one or more simulation algorithms may be applied to the transformed network model to reduce the number of routing decisions simulated.