Network Topology Discovery Using Weighted Link Reduction
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Solution Overview
Problem
Current methods for discovering network topology are inefficient and inaccurate, especially when relying on a single type of data, as they fail to effectively map entire networks, necessitating the use of multiple network communication data types for accurate discovery.
Innovation Solution
A method that collects network activity data from various protocols (SNMP, ping, SSH/Telnet, NMAP, etc.) to generate synthetic links, assigns weights based on link types, and applies reduction logic to determine the most direct connections, thereby creating a comprehensive and accurate network topology map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple types of network communication data are collected and processed, then the accuracy and completeness of network topology discovery is improved, but the complexity of the discovery system increases
Solution Approach 1:
The system segments the network topology discovery process into distinct functional modules: a data collector module that gathers data from multiple protocols (LLDP, CDP, STP, MAC tables, ARP tables), a correlation engine that processes the data, and a topology mapping unit that generates the final topology map. This segmentation allows each module to handle specific tasks independently, improving accuracy while managing complexity through modular design.
Solution Approach 2:
The correlation engine serves as a universal component that handles multiple types of network communication data from various protocols and sources. It correlates data from LLDP, CDP, STP, MAC address tables, and ARP tables through a single unified processing mechanism, eliminating the need for separate processing systems for each data type and thus reducing overall system complexity while maintaining high discovery accuracy.
2Measurement precision
If multiple types of network communication data are collected and processed, then the accuracy and completeness of network topology discovery is improved, but the time and computational resources required increase
Solution Approach 1:
The data collector module performs preliminary data collection from multiple protocols and sources simultaneously, gathering LLDP, CDP, STP, MAC table, and ARP data in parallel before correlation. This preliminary action allows the correlation engine to receive pre-organized data from all sources, reducing the time required for processing and enabling faster topology discovery while maintaining comprehensive data accuracy.
3Ease of operation
If a reduction method is applied to identify the most direct links, then the clarity of the network topology map is improved, but the complexity of the correlation logic increases
Solution Approach 1:
The correlation engine applies parameter changes by evaluating multiple criteria (protocol reliability, link directness, data consistency) to determine the most direct links between network devices. It transforms raw correlation data into ranked link suggestions, changing the parameters from raw data to evaluated metrics, which simplifies the final topology map while the underlying logic handles the complexity of multi-criteria evaluation.
Data Source
AI summary
Example methods, apparatuses, and systems are presented for discovery of how network devices are interconnected. A group of network devices may be discovered that are associated with each other may be determined. Using a system of the present disclosures, information is gathered from network devices using networking protocols and links are identified. Protocols suggest how two or more network devices are connected to each other. Every interface on the physical or virtual device may be associated with multiple types of links identified by various protocols. A weight is assigned to each link using a weight mapping table. Next a method to reduce the number of multiple links is applied. The reduction method uses the link associated with an interface with the lowest weight to map the network topology, in some embodiments.