Self-Healing Data Protection via Weighted Script Matching
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Solution Overview
Problem
Current large-scale data protection systems require extensive manual interaction for troubleshooting and remediation, as executing relevant scripts can cause side effects and additional issues, necessitating a system that automatically matches failed operations to appropriate scripts without adverse effects.
Innovation Solution
A self-healing data protection system that leverages a support remediation knowledge database to identify issues and automatically execute scripts, using weighted attributes to match failed operations with scripts that are most likely to fix problems without causing side effects, thereby minimizing service unavailability and data loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If scripts are executed to automatically solve issues, then productivity is improved, but reliability deteriorates due to potential side effects and additional issues
Solution Approach 1:
The system performs preliminary analysis by extracting attributes from alert messages and comparing them against script requirements before execution. This preliminary matching action ensures that only scripts with high compatibility scores are selected, preventing inappropriate script execution that could cause side effects while maintaining automated issue resolution capability
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring system state changes after script execution and using this information to refine future script selection. The feedback loop allows the system to learn from past executions, improving reliability by avoiding scripts that previously caused unintended consequences while maintaining productivity through automated responses
2Reliability
If manual technical support intervention is used, then reliability is maintained through human judgment, but productivity deteriorates due to extended resolution time
Solution Approach 1:
The system enables self-service by automatically analyzing alert messages, extracting relevant attributes, and selecting appropriate scripts without human intervention. This self-service capability maintains reliability through consistent attribute-based matching while dramatically improving productivity by eliminating manual diagnosis and resolution time
Solution Approach 2:
The system replaces the mechanical process of manual technical support with an automated computational system that extracts attributes from alerts and matches them against script requirements. This substitution maintains diagnostic accuracy through systematic attribute analysis while improving resolution speed by eliminating human response delays
3Adaptability or versatility
If comprehensive scripts are used to cover multiple issues, then adaptability is improved, but harmful factors increase due to side effects on unrelated system parts
Solution Approach 1:
The system applies local quality by extracting specific attributes from alert messages and matching them against corresponding script requirements, ensuring that only the necessary portions of scripts are executed. This attribute-based local matching prevents comprehensive scripts from affecting unrelated system parts while maintaining adaptability to handle various issue types through selective attribute pairing
Solution Approach 2:
The system segments the script execution process by breaking down comprehensive scripts into attribute-specific actions. By matching individual alert attributes with corresponding script segments, the system executes only the relevant portions needed to resolve the specific issue, preventing side effects on unrelated system components while maintaining versatile coverage across different issue types
Data Source
AI summary
Embodiments automate the timely remediation of issues by matching failed operations to a script that is configured to fix the problem or problems without causing additional side effects, thus minimizing unavailability of services and loss of data. A method detects a backup operation alert message sent from a data manager or storage system that encodes one or more attributes affected by the backup operation, wherein the attributes are weighted to reflect a percentage importance of the a corresponding script to fix the problem. A script library is scanned to identify scripts that match the alert message. A component generates a score for each script, where the score factors the relevance of the script to the backup operation based on the weighted attributes, and executes the script with the highest score, as the script most likely to fix the issue causing the alert message.