Cognitive Health Check Script for Distributed System Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Distributed computing systems face inefficiencies due to complexity and interconnectivity, making it difficult to diagnose and resolve issues effectively, leading to high maintenance costs and user productivity losses.
Innovation Solution
A method and computer program product that utilize cognitive analysis to identify components in a distributed computing system, generate scripts for health checks, and automatically resolve performance issues, incorporating machine learning to improve efficacy and efficiency over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual diagnosis and resolution methods are used in distributed computing systems, then human expertise can handle complex issues, but maintenance costs increase and productivity is lost due to manual interventions
Solution Approach 1:
The system implements self-service through automated health check scripts that autonomously evaluate system components, diagnose issues, and apply resolutions without human intervention. The cognitive analysis engine automatically processes health check data, identifies patterns, and executes remediation procedures, enabling the system to serve itself in maintaining optimal performance.
Solution Approach 2:
The system performs preliminary actions by continuously executing health checks and cognitive analysis to identify and resolve issues before they impact system productivity. The automated approach proactively monitors components and applies resolutions in advance, preventing downtime rather than reacting to failures after they occur.
2Extent of automation
If automated health checks are implemented, then manual interventions are reduced, but system complexity increases due to the need for cognitive analysis and pattern recognition
Solution Approach 1:
The cognitive analysis engine serves as an intermediary layer between the automated health check scripts and the issue resolution procedures. This intermediary component processes raw health check data, applies pattern recognition algorithms, and translates findings into actionable resolutions, thereby managing system complexity through modular architecture while maintaining high automation levels.
Solution Approach 2:
The system segments the automated monitoring process into distinct modular components: health check execution, data collection, cognitive analysis, pattern recognition, and resolution execution. This segmentation allows each component to be independently developed and maintained, reducing overall system complexity while enabling comprehensive automation.
3Measurement precision
If comprehensive health checks are performed on all components, then issue detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by performing health checks on critical system components with higher frequency and detail, while using reduced monitoring for less critical components. The cognitive analysis engine dynamically adjusts the depth of analysis based on component importance and historical data, achieving high detection accuracy for critical issues without the processing overhead of exhaustive checks on all components.
Solution Approach 2:
The system implements feedback mechanisms where cognitive analysis results from previous health checks inform future monitoring strategies. The pattern recognition algorithms learn from historical data to identify which components require intensive monitoring, dynamically adjusting health check frequency and depth to maintain high detection accuracy while optimizing processing time based on real-time system conditions.
Data Source
AI summary
A method, computer program product, and a system where a processor(s) obtains data comprising issues and respective issue resolution plans for the issues experienced in a distributed computing system. The processor(s) cognitively analyzes the data to identify components of the distributed computing system for status evaluation and generates a script to perform a health check on the components and to automatically resolve performance issues of the components identified during the health check. The processor(s) executes the script, where for each component, the processor(s) determined if a status of the component indicates a performance issue and if not, update the data utilized in the cognitive analysis. If yes, the processor(s) attempts to resolve the status and updates the data with the results.