Self-Healing Rule Optimization via Machine Learning Feedback
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Solution Overview
Problem
Existing computer system processes lack effective self-healing mechanisms, as current solutions do not analyze or optimize self-healing rules, leading to delayed task completion and negative impacts on customer satisfaction and financials due to unaddressed failures.
Innovation Solution
A system and method that utilizes a machine learning model to collect data on self-healing rule usage, analyze failures, and generate recommendations for optimizing self-healing rules, thereby improving their effectiveness in resolving computer process failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manually configured self-healing rules are applied to resolve failures, then failures can be addressed automatically, but the rules cannot be optimized and performance deteriorates over time
Solution Approach 1:
The system collects data on failure resolutions and feeds it back to the machine learning model, which then generates optimized self-healing rules. This closed-loop feedback mechanism allows the system to learn from actual failure patterns and continuously improve rule effectiveness without manual reconfiguration.
Solution Approach 2:
The machine learning model automatically analyzes failure data and generates optimized self-healing rules without human intervention. The system serves itself by using its own operational data to improve its performance, eliminating the need for manual rule optimization while maintaining high reliability.
2Device complexity
If existing self-healing rules are used without analysis, then system complexity is minimized, but productivity decreases due to unresolved failures
Solution Approach 1:
The patent replaces manual mechanical rule management with an automated machine learning system. The ML model automatically analyzes failure data, identifies patterns, and generates optimized rules, substituting the manual process with an intelligent system that improves productivity without significant complexity increase.
Solution Approach 2:
The system changes the parameters of self-healing rules through automated ML optimization. By analyzing historical failure data and resolving patterns, the system adjusts rule parameters dynamically to improve task completion speed while maintaining manageable system complexity through automation.
3Reliability
If manual intervention is used to optimize self-healing rules, then rule effectiveness can be improved, but loss of time increases due to manual analysis requirements
Solution Approach 1:
The machine learning model performs self-optimization by automatically analyzing failure data and generating improved rules without human intervention. This self-service capability eliminates manual optimization time while maintaining high rule effectiveness, as the system learns from its own operational history continuously.
Solution Approach 2:
The system performs preliminary analysis of failure data continuously in the background, preparing optimized rules in advance. This preliminary action eliminates the need for manual analysis time by having the ML model pre-process and optimize rules automatically before they are needed for failure resolution.
Data Source
AI summary
As described herein, a system, method, and computer program are provided for intelligent self-healing optimization for fallout reduction. A set of self-healing rules are stored that are configured to provide resolutions to failures detected in a computer process. Data associated with use of the self-healing rules is collected. The data is processed using a machine learning model to generate one or more recommendations for optimizing the set of self-healing rules. The one or more recommendations are output.