Network Snapshot Modeling for Reconfiguration Path Forecasting
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Solution Overview
Problem
Large-scale communication networks face challenges in efficiently predicting and mitigating the impact of network reconfigurations, such as node additions, removals, or changes, which can disrupt data packet routing and network performance.
Innovation Solution
A computer-implemented method that generates network snapshots and models, allowing analysts to simulate and predict the effects of proposed changes by applying them to a network model, using BGP files and update reports to determine optimal data packet paths before actual implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network snapshots and models are generated to simulate and predict the effects of proposed changes, then network performance stability is improved, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by generating network snapshots and routing models before actual reconfigurations are implemented. Analysts can simulate proposed changes on historical snapshots to predict impacts on data packet paths, allowing proactive identification and mitigation of potential performance disruptions before they occur in the live network.
Solution Approach 2:
The system creates copies of the network state in the form of snapshots containing topology, RIB, and other configuration data at specific points in time. These snapshot copies can be freely manipulated and analyzed without affecting the actual network operations, enabling safe simulation of reconfiguration scenarios.
2Measurement precision
If detailed network snapshots with topology and RIB data are accessed and processed, then path analysis precision is improved, but data processing time and resource consumption increase
Solution Approach 1:
The system pre-processes and stores network data into structured snapshots containing topology, RIB, and other relevant information at specific time points. This preliminary organization of data enables efficient querying and analysis when reconfiguration scenarios are simulated, reducing processing time compared to analyzing raw network data.
Solution Approach 2:
The network state is segmented into distinct components within snapshots, including topology data, RIB data, and other configuration information. This segmentation allows the system to access and process only the specific data elements needed for particular analysis tasks, improving efficiency while maintaining comprehensive path analysis capability.
Data Source
AI summary
This disclosure pertains to computer-assisted methods, apparatus, and systems for forecasting network performance responsive to network reconfigurations in large scale communication networks.