Automated CI Issue Resolution via Intelligent Sequencing Engine
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Solution Overview
Problem
Current methods for resolving configuration item (CI) issues in computing environments are time-consuming, prone to errors, heavily dependent on subject matter experts, and lack automation, making it difficult to modify or pause solution sequences effectively.
Innovation Solution
A system and method utilizing an intelligent sequencing engine that identifies problem types and domains of CIs, checks for diagnosis and resolution sequences, updates them, and executes them iteratively based on outputs, allowing for automated resolution, visualization, and modification of solution sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a subject matter expert manually executes a sequence of actions to resolve CI issues, then the solution can be customized and adapted to specific problems, but the process consumes excessive time and is prone to human error
Solution Approach 1:
The system pre-defines multiple diagnosis and resolution sequences that have been prepared in advance for various CI issue scenarios. When a CI issue is detected, the system automatically selects and executes the appropriate pre-prepared sequence without requiring manual analysis, thereby reducing resolution time while maintaining reliability through proven solution paths.
Solution Approach 2:
The system enables automated self-diagnosis and self-resolution of CI issues by executing diagnosis and resolution sequences autonomously. The computer automatically performs the functions previously requiring human experts, reducing both time consumption and human error while maintaining solution quality through systematic automated processes.
2Adaptability or versatility
If subject matter experts manually analyze and execute solution sequences, then complex problems can be addressed with expert judgment, but the dependency on experts increases and solutions cannot be modified once executed
Solution Approach 1:
The system allows dynamic modification of diagnosis and resolution sequences during and after execution. Users can add, remove, or modify steps in the sequences based on changing requirements or newly discovered issues. The system maintains flexibility by allowing sequences to be updated without requiring complete re-execution, enabling adaptation while reducing expert dependency through automated management of the sequences.
3Reliability
If multiple subject matter experts provide different solution sequences for the same CI issue, then diverse perspectives can improve solution quality, but consistency and reproducibility of solutions deteriorate
Solution Approach 1:
The system segments the solution process into distinct, standardized diagnosis and resolution sequences that can be independently selected and executed. Each sequence represents a discrete, reproducible solution path for specific CI issue types. This segmentation ensures consistency by using the same standardized sequences across different experts while maintaining the ability to handle diverse scenarios through the collection of available sequences.
4Productivity
If the system executes diagnosis and resolution sequences automatically, then resolution speed increases, but the ability to pause and verify solution correctness at intermediate stages is reduced
Solution Approach 1:
The system incorporates feedback mechanisms that allow monitoring and verification of solution execution at intermediate stages. Users can pause the automated execution, review the current state and next proposed actions, verify correctness against expected outcomes, and then resume or modify the sequence as needed. This maintains high productivity through automation while preserving control for verification when required.
Data Source
AI summary
A system and method for automated resolution of events in a computing environment is provided. Problem types are identified from the events which are associated with configuration items (CIs) at issue. Further, domains of the CIs at issue are identified. Domains represent types of the CIs at issue. Existence of diagnosis and resolution sequences is checked which is based on identified problem types and domain associated with the CI types. Thereafter, diagnosis and resolution sequences are fetched, where sequences fetched at a first instance are at least in part updated with one or more new sequences. Fetched sequences are executed iteratively such that a next step of the executed diagnosis and resolution sequence is based on an output of a first step of the executed diagnosis and resolution sequence.