Undirected Graph Object Discovery for Configuration Parameter Optimization
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Solution Overview
Problem
Modern computer systems with extensive configuration parameters face challenges in optimizing the reading of single values and selecting multiple parameters efficiently, particularly in large configuration trees with many objects of different types and properties.
Innovation Solution
A method and apparatus for discovering interdependent computing objects within an undirected graph structure by performing object identification, relations identification, and selecting objects through set operations like intersection, union, and combination, based on defined properties and relations, to optimize the retrieval of relevant objects and their properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to read and select configuration parameters in large computer systems, then completeness of parameter retrieval is maintained, but efficiency and speed of parameter reading deteriorate
Solution Approach 1:
The patent segments the large configuration tree into multiple Organization Areas (OAs), which are then further divided into subsets. This hierarchical segmentation allows the system to process and search for parameters in smaller, manageable units rather than traversing the entire configuration tree, significantly improving parameter reading efficiency while reducing time loss.
Solution Approach 2:
The patent performs preliminary actions by pre-identifying and caching frequently accessed parameters and their relationships within each OA subset. This preliminary processing creates optimized data structures that enable rapid parameter retrieval during operation, eliminating the need for repeated full-tree traversals and thus improving productivity while minimizing time loss.
2Loss of information
If all objects and properties are processed in large configuration trees, then completeness of information is maintained, but system complexity increases
Solution Approach 1:
The configuration tree is segmented into multiple Organization Areas (OAs) and further divided into subsets, allowing the system to process information in manageable chunks. This segmentation maintains information completeness by ensuring all OAs are processed while reducing complexity by limiting the scope of each processing operation to a specific subset rather than the entire configuration tree.
Solution Approach 2:
The patent introduces a new dimensional organization by creating a hierarchical structure with multiple levels: configuration tree → Organization Areas (OAs) → subsets. This dimensional change transforms the flat, monolithic configuration processing into a multi-level structured approach, maintaining complete information coverage while systematically reducing processing complexity at each level.
3Productivity
If traditional parameter selection methods are used, then all parameters are considered, but selection speed and optimization capability deteriorate
Solution Approach 1:
The patent implements dynamic parameter selection by allowing the system to adaptively choose which Organization Areas and subsets to process based on current system state and requirements. This dynamic approach enables rapid parameter selection by focusing computational resources on relevant OAs while maintaining flexibility to adjust selection criteria based on changing conditions, thus improving both speed and adaptability.
Solution Approach 2:
The system dynamically changes selection parameters such as OA priority weights, subset filtering criteria, and relationship importance metrics based on current system conditions. This parameter adjustment capability allows the system to optimize parameter selection speed for specific scenarios while maintaining versatility to handle different selection requirements, resolving the contradiction between speed and flexibility.
Data Source
AI summary
Various embodiments relate to a method and apparatus for discovering a group of interdependent computing objects within an undirected graph structure of objects in a computing environment, the method comprising the steps of performing object identification, performing relations identification, selecting objects by performing the steps comprising defining required properties, matching the required properties with the properties for each of the objects, dropping the objects which have at least one of the properties which do not match with the required properties, making a selection by performing object intersection on the objects by performing the steps of reading location and the relations for each of the objects, selecting a group including each of the objects in the sets of objects which are identical based on the relations for each of the objects, determining whether each of the objects in the group are related to each of the objects in each of the sets of objects, and determining whether at least one of the objects in each of the sets of objects is related to at least one of the objects in another set of the sets of objects.