AI Change Impact Modeling for Dependent Computing Systems
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Solution Overview
Problem
In complex, interdependent computing systems, accurately assessing the impact of changes across disparate systems is challenging due to intricate interdependencies, making it difficult for decision-makers to understand and quantify the effects of changes, leading to potential negative impacts.
Innovation Solution
An automated system using artificial intelligence, specifically machine learning models, to identify and quantify the impact of changes on dependent systems through a User and Object Metadata and Relation Repository (UOMRR) and Reactive Change History Repository (RCHR), generating impact scores and reports to inform decision-makers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment methods are used to evaluate change impacts in complex computing systems, then decision-makers can understand the evaluation process, but the assessment accuracy and completeness deteriorate due to the intricate interdependencies across multiple disparate systems
Solution Approach 1:
The patent introduces an automated impact assessment system that acts as an intermediary between the complex computing systems and decision-makers. This system automatically traverses the spiderweb-like interdependencies across disparate computing environments, collecting and analyzing metadata to quantify change impacts without requiring manual navigation of the complex system relationships
Solution Approach 2:
The patent replaces manual assessment mechanisms with automated computational systems. Machine learning models and automated traversal algorithms substitute human analysts, enabling precise measurement of change impacts across complex interdependencies that would be impractical to assess manually
2Loss of information
If comprehensive metadata collection is performed across all computing devices to capture full system interdependencies, then the holistic view of system state improves, but the data processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by continuously collecting and storing metadata from computing devices before changes occur. The automated impact assessment system pre-traverses the system landscape and maintains updated information about interdependencies, so when a change is proposed, the assessment can be performed quickly using pre-collected data rather than gathering information in real-time
Solution Approach 2:
The patent segments the comprehensive metadata collection into targeted queries based on the specific change being assessed. Instead of collecting all possible metadata continuously, the system identifies relevant computing devices and metadata types related to the proposed change, reducing processing time while maintaining assessment accuracy
3Productivity
If automated impact assessment systems are implemented to quickly evaluate changes, then decision-making speed improves, but the complexity of the assessment methodology and system requirements increases
Solution Approach 1:
The patent implements a universal automated impact assessment system that can evaluate changes across multiple disparate computing environments and system types. The system performs multiple functions including metadata collection, dependency traversal, impact calculation, and report generation, reducing the need for separate assessment tools for different system configurations
Solution Approach 2:
The patent uses parameter changes in the form of configurable assessment models and metadata schemas. The system allows customization of assessment parameters based on specific organizational needs and system characteristics, enabling fast automated assessment while adapting to different complexity levels through parameter adjustment rather than structural changes
Data Source
AI summary
Mechanisms are provided for representing impacts of changes to objects in a computing system on dependent objects of the computing system. Metadata from monitored computing devices of the computing system is collected, where the metadata is associated with objects of the computing devices. A user and object metadata and relation repository (UOMRR) is generated from the metadata, where the UOMRR comprises a lineage graph that represents a dependency of the objects. A change request is received for changing a resource of a computing device in the monitored computing devices. A source object is identified in the lineage graph corresponding to the change request, and paths in the lineage graph corresponding to the source object are identified. Impact scores are calculated for each of the paths, where the impact scores quantify an impact of the change request on dependent objects of the corresponding path.