IGP Convergence Measurement via Distributed Probe Segmentation
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Solution Overview
Problem
Monitoring and assessing IGP convergence performance in large networks is challenging due to network size, short convergence times, unpredictable topological failures, and the need for detailed measurements.
Innovation Solution
A distributed system for measuring IGP convergence that receives probes, temporarily stores data, identifies convergence events, and records parameters like loss distance, latency, and jitter, using a scalable number of probing instances and logs to assess convergence performance with limited memory and probe generation rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a distributed system uses a scalable number of probing instances to monitor IGP convergence, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the monitoring function into multiple distributed probe generators deployed across different network nodes. Each probe generator independently monitors specific network paths, dividing the complex task of IGP convergence measurement into manageable distributed units that can be scaled independently
Solution Approach 2:
The patent introduces intermediate components including probe receivers at boundary network elements that aggregate probe data, and a convergence performance monitor that processes measurements. These intermediaries simplify the architecture by providing structured interfaces between probe generators and the central analysis system
2Measurement precision
If the system records detailed probe data for convergence events, then measurement precision is improved, but loss of substance increases due to limited memory
Solution Approach 1:
The system extracts and prioritizes only the essential convergence event data for permanent storage, separating critical measurements (convergence timing, affected paths) from routine probe data. This extraction approach ensures that limited memory resources are dedicated to storing the most valuable convergence analysis information
Solution Approach 2:
The system performs preliminary filtering and aggregation of probe data before storage, pre-processing measurements to identify and flag convergence events. By preparing data in advance and organizing it according to importance, the system maximizes the utility of stored data while minimizing memory requirements
3Adaptability or versatility
If the system monitors large networks with many nodes, then adaptability is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system transitions from monitoring individual network parameters to measuring convergence performance in the time dimension. By focusing on temporal characteristics of convergence events rather than spatial network complexity, the system simplifies measurement while maintaining adaptability to networks of any size
Solution Approach 2:
The patent changes the monitoring parameters from detailed per-node state tracking to aggregate convergence performance metrics. By measuring convergence time, affected path count, and recovery duration at the network level rather than individual node level, the system reduces measurement complexity while preserving adaptability
4Measurement precision
If the system captures convergence events occurring in hundreds of milliseconds, then measurement precision is improved, but loss of time increases due to data processing requirements
Solution Approach 1:
The system implements event-driven processing that skips routine data collection during normal operation and rushes through detailed measurement only when convergence events are detected. This selective approach captures precise convergence timing data while minimizing overall processing time by focusing computational resources only when needed
Data Source
AI summary
A system and method for measuring convergence performance in a network are disclosed. In one embodiment, the method includes receiving a plurality of probes at a network device, temporarily storing data from at least a portion of the received probes and deleting at least a portion of the temporarily stored data at regular intervals, and receiving a packet indicating a convergence event and storing data from probes received over a predetermined period.