Hyper-Converged Infrastructure Discovery Pattern for Cluster Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing and visualizing the configuration items within a network, particularly in a hyper-converged infrastructure (HCI) computing cluster, is challenging due to the complexity of discovering and mapping servers, applications, and their relationships, which is essential for proper utilization and maintenance of network services.
Innovation Solution
A discovery pattern is developed to characterize and map the configuration items of a computing cluster, including servers, applications, and storage devices, by establishing relationships between them, and storing this information in a database for status determination and graphical representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a discovery pattern is used to detect and map configuration items in an HCI cluster, then the completeness of configuration information is improved, but the complexity of the discovery process increases
Solution Approach 1:
The discovery pattern segments the HCI cluster configuration discovery into distinct phases: detecting computing devices, identifying storage devices, establishing relationships, and mapping configuration items. This segmentation allows each phase to be handled systematically, reducing overall process complexity while ensuring complete configuration information is captured.
Solution Approach 2:
The discovery pattern performs preliminary actions by first detecting computing devices and their basic properties before proceeding to identify storage devices and establish relationships. This preliminary structuring of the discovery process ensures that configuration information is collected in a logical sequence, improving completeness without overwhelming system complexity.
2Ease of operation
If relationships between configuration items are established and mapped, then the usability of network management is improved, but the processing time increases
Solution Approach 1:
The mapping process is segmented into relationship establishment phases and visualization phases. Configuration items are first detected and relationships are established in structured steps, then the mapped information is presented through a graphical user interface. This segmentation allows processing to be efficient while usability is enhanced through the visual representation.
Solution Approach 2:
A graphical user interface acts as an intermediary between the mapped configuration data and the user. The interface translates complex relationship mappings into visual representations that are easy to interpret, improving usability without requiring the underlying processing to be faster.
3Reliability
If comprehensive discovery of HCI cluster components is performed, then the reliability of status determination is improved, but the device complexity increases
Solution Approach 1:
The comprehensive discovery process is segmented into detecting computing devices, identifying storage devices, establishing relationships, and mapping configuration items. Each segment focuses on a specific aspect of the HCI cluster, ensuring reliable status determination through systematic coverage while managing system complexity through structured organization.
Data Source
AI summary
A system may include a database, where a managed network includes a computing cluster that provides networking, storage, and virtualization services distributed across a plurality of computing devices, where each computing device can execute one or more respective software applications and comprises: (i) a respective controller, and (ii) a respective storage device, and where the storage devices of the plurality of computing devices collectively form a storage pool. The system may also include a proxy server application configured to: request and receive computing cluster data that identifies the computing cluster; request and receive storage pool data that identifies the storage pool; request and receive storage container data that identifies storage containers of the storage pool; request and receive controller data that identifies the controllers of the plurality computing devices; and provide, to the database, the computing cluster data, the storage pool data, the storage container data, and the controller data.