Network Topology Estimation via Event Correlation
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Solution Overview
Problem
Current communication networks face challenges in managing networks with a priori unknown or dynamically changing topologies, limiting operators' knowledge and making network configuration, management, and troubleshooting difficult.
Innovation Solution
A management node that obtains and correlates network events across multiple nodes to identify clusters and determine a topology model, using time correlation and probabilistic metrics to refine and update the network topology, allowing for efficient handling of alarms and reconfigurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the network topology is complex and dynamically changing, then the network can be flexible and adaptive, but the operator's knowledge and control capability deteriorate
Solution Approach 1:
The system continuously monitors network events from multiple nodes and feeds this information back to the management node. The management node uses this feedback to automatically update and refine the topology model over time, transforming raw event data into actionable topology knowledge without requiring manual intervention or prior configuration information.
Solution Approach 2:
The management system automatically discovers and updates the network topology through self-service mechanisms. By correlating network events and autonomously building topology models, the system eliminates the need for operators to manually maintain topology knowledge, allowing the network to self-characterize its structure dynamically.
2Measurement precision
If dedicated reporting functionalities are added to individual nodes, then topology discovery capability is improved, but device complexity increases
Solution Approach 1:
The system uses existing, multi-functional network events for topology discovery rather than adding dedicated reporting functionalities. Standard network events serving multiple purposes (performance monitoring, fault detection, etc.) are repurposed for topology characterization, eliminating the need for separate topology discovery mechanisms at each node.
Solution Approach 2:
The management node acts as an intermediary that collects and correlates events from multiple nodes. Instead of requiring each node to have sophisticated topology discovery capabilities, the management node performs the complex correlation and model building, simplifying individual node functionality while achieving comprehensive topology awareness.
3Measurement precision
If manual network configuration and management is performed, then control precision is improved, but productivity and operational efficiency deteriorate
Solution Approach 1:
The system automatically characterizes network topology and groups related alarms through self-service mechanisms. By autonomously analyzing network events and building topology models, the system eliminates manual topology discovery and alarm correlation tasks, significantly improving operational efficiency while maintaining precise control through automated decision-making.
4Adaptability or versatility
If the transport network uses leased lines and third party services, then network versatility is improved, but reliability of topology knowledge deteriorates
Solution Approach 1:
The system continuously monitors network events from actual network behavior and uses this feedback to dynamically update the topology model. This event-driven approach provides reliable topology knowledge regardless of the underlying network composition, as it discovers topology through observed behavior rather than relying on configuration data from multiple vendors or third parties.
Data Source
AI summary
A management node (100) obtains indications of network events occurring at a plurality of nodes (210-1, 210-2, 210-3, 210-4, 220-1, 220-2 , 230-1, 230-2) of the communication network. Further, the management node (100) performs a correlation of times of the indicated network events. On the basis of the correlation, the management node (100) identifies clusters of nodes (210-1, 210-2 , 210-3, 210-4, 220-1, 220-2, 230-1, 230-2) with correlated network events. On the basis of the clusters, the management node (100) determines a topology model of the communication network.