Self-Healing Data Protection Script Matching
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Solution Overview
Problem
Current data protection systems require extensive manual intervention for troubleshooting and remediation, leading to inefficiencies and increased reliance on technical support, as executing relevant scripts can cause side effects and additional issues.
Innovation Solution
A self-healing data protection system that automatically matches failed operations to the most appropriate remedial scripts using weighted attributes, minimizing side effects by calculating scores based on attribute matching and penalties for non-relevant attributes, thereby automating the remediation process.
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 implements feedback mechanisms by monitoring system state changes after script execution and automatically rolling back if negative effects are detected. The self-healing service continuously evaluates whether executed scripts achieve the intended remediation without causing harmful side effects, using feedback loops to adjust future script selection and execution decisions.
Solution Approach 2:
The system employs self-service through automated self-healing services that independently identify issues, select appropriate remediation scripts, execute them, and monitor outcomes without requiring manual technical support intervention. The system serves itself by automatically detecting when scripts cause problems and autonomously correcting or reversing those actions.
2Measurement precision
If multiple scripts are considered for matching, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system extracts and isolates specific attributes from alert messages and matches them against corresponding attributes in scripts. By focusing only on relevant attributes rather than analyzing entire script contents or alert texts, the system achieves precise matching while keeping the complexity manageable through selective attribute comparison.
Solution Approach 2:
The system changes parameters by assigning weights to different attributes based on their importance for matching. This allows the system to prioritize certain attributes over others, improving matching precision while managing complexity through weighted scoring rather than treating all attributes equally.
Data Source
AI summary
Embodiments help timely remediation of issues in a data protection system by automatically evaluating scripts configured to address the issues. The system has a bounded list of possible attributes that are deemed important by system administrators. Each attribute is assigned a System Impact Score (SIS) along a defined scale. A self-healing processing component monitors the state of each attribute over time. The scripts are evaluated through repeated execution and use of the attribute monitoring to determine which attributes are affected by a script. Weights are assigned to each attribute affected by a script to aid in the selection of scripts most likely to remediate an actionable issue. Regularly performing script evaluation and attribute weighting allows for updating of scripts with an accurate list of attributes to overcome problems associated with manual updates.