Network Topology Graph Engine for Automated Large-Scale Visualization
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Solution Overview
Problem
Current computer network topology mapping tools are unable to automatically create and display a graph representation of network objects communicating with an application at a large scale, particularly failing to handle changes, critical metrics, and mixed data with a time dimension.
Innovation Solution
A system that uses an efficient IT graph engine to continually discover and display a graph representation of network topology over time by collecting and processing configuration and time series information from network objects, allowing for automated and manual input of topology information, and generating a graph representation that includes unique keys, relationships, and time dimension data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If current network topology mapping tools are used, then basic topology information can be obtained, but automatic creation and display of graph representation at large scale is unable to be achieved
Solution Approach 1:
The system segments the complex topology mapping task into distinct functional modules: a graph engine that creates graph representations, a topology information manager that stores and retrieves topology data, and a user interface that displays the graphs. This segmentation enables automatic graph creation at large scale by distributing the computational workload across specialized components rather than requiring a monolithic complex system.
Solution Approach 2:
The patent introduces a graph engine as an intermediary component between the topology information manager and the user interface. This graph engine automatically transforms raw topology data into visual graph representations, serving as a mediator that handles the complex transformation logic and enables automated graph creation without requiring the entire system to be overly complex.
2Measurement precision
If repeated creating and displaying of graph representation is performed to compare changes, then topology changes can be detected, but handling of missing critical data and large scale topology becomes problematic
Solution Approach 1:
The system performs preliminary actions by storing complete topology information in the topology information manager before graph representation is needed. This pre-stored topology data serves as a reference baseline that enables accurate change detection when graphs are repeatedly created and compared, without requiring re-collection of large volumes of topology data each time.
Solution Approach 2:
The patent implements copying by creating multiple graph representations from the same topology information at different time points. These graph copies are then compared to detect changes. This copying approach enables precise change detection while avoiding the need to process the entire original topology data volume repeatedly, as the graph structures serve as compact representations for comparison.
3Adaptability or versatility
If graph representation includes group resources like clusters or services, then comprehensive topology visualization is achieved, but data processing complexity increases
Solution Approach 1:
The graph engine is designed with universal functionality to handle multiple types of topology elements (individual network objects, clusters, services) through a unified graph representation model. This multi-functionality enables comprehensive topology visualization without requiring separate processing logic for each data type, thereby managing complexity while maintaining versatility.
Solution Approach 2:
The patent resolves data processing complexity by introducing a hierarchical dimension to the graph representation. Individual network objects, clusters, and services are represented at different levels of abstraction in the same graph structure. This dimensional organization allows the system to handle mixed data types comprehensively while managing complexity through hierarchical structuring rather than flat processing of all elements equally.
4Loss of information
If topology information is collected from multiple sources including flow logs and metrics, then comprehensive topology data is obtained, but data collection and processing time increases
Solution Approach 1:
The system merges multiple data collection sources (flow logs, metrics, configuration data) into a unified topology information structure managed by the topology information manager. This consolidation enables comprehensive topology information to be obtained while reducing processing time by handling all data sources through a single integrated interface rather than processing each source separately.
Solution Approach 2:
The topology information manager implements self-service by automatically collecting, normalizing, and storing topology data from multiple sources without requiring manual intervention for each data type. This automated self-service approach ensures complete topology information is captured while minimizing the time investment required for data collection and processing.
Data Source
AI summary
There are disclosed devices, system and methods for creating and displaying a graph representation of a topology of a computer network of physical network objects for an application. A first physical network object selects portions of sets of data messages being sent over a period of time by the application and related network objects; and collects network configuration and time dimension information from the portions of messages. A second physical network object receives the information and uses it to determine topology information for the application over time, which includes unique keys of, types of objects of, types of relationships between pairs of, groupings of, metrics data of, and time dimension data for the physical network objects. The topology information is stored and queried at the second object to create and display various graph representations of the topology information as it changes over time.