Machine Learning Data Backup Policy Adaptation
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Solution Overview
Problem
Existing data backup systems often fail to include frequently accessed files due to inaccurate include/exclude lists, finger checks, or outdated filename lists, leading to potential data loss during backup operations.
Innovation Solution
A method and system that utilize machine learning tools to identify frequently accessed files based on workload behavior, generate lists of these files, compare them to active backup policy rules, and update the rules to ensure accurate data backup, shifting from policy-based to behavior-based management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If policy-based management system is used for data backup, then backup operations can be automated and managed, but data loss occurs due to inaccurate include/exclude lists, finger checks, or outdated filename lists
Solution Approach 1:
The system continuously monitors actual file access patterns and workload behavior, comparing them against backup policy rules. When mismatches are detected (files that should be backed up but are excluded, or files excluded but should be included), the system generates feedback reports and automatically updates backup rules to align with actual workload needs, preventing data loss while maintaining automation
Solution Approach 2:
The backup system performs self-diagnosis and self-correction by automatically analyzing file access patterns, identifying mismatches between policy rules and actual needs, and updating its own backup rules without requiring manual intervention. This self-service capability ensures the system adapts to changing workload patterns and maintains accurate backup coverage
2Measurement precision
If manual monitoring and adjustment of backup include/exclude lists is performed, then backup accuracy can be maintained, but time consumption and operational complexity increase
Solution Approach 1:
The system automatically monitors file access patterns, analyzes workload behavior, and updates backup rules without requiring manual intervention. It performs self-diagnosis, identifies mismatches between policy rules and actual needs, and corrects its own backup configurations, eliminating the need for manual monitoring while maintaining high accuracy
Solution Approach 2:
The patent replaces manual mechanical processes (manual monitoring and adjustment of include/exclude lists) with automated electronic systems that use machine learning and data analysis to identify file access patterns and automatically update backup rules, significantly reducing time consumption while maintaining or improving accuracy
3Adaptability or versatility
If backup rules are strictly enforced based on policy, then compliance is maintained, but frequently accessed files may be excluded due to inaccurate or outdated rules
Solution Approach 1:
The backup system transitions from static policy-based rules to dynamic rules that automatically adapt to changing workload patterns. It continuously learns from actual file access behavior and adjusts backup rules in real-time, ensuring that frequently accessed files are consistently included in backups while maintaining compliance with organizational policies
Solution Approach 2:
The system monitors actual backup execution and file access patterns, using this feedback to identify when policy rules diverge from actual needs. This feedback mechanism enables the system to automatically adjust rules to improve backup coverage for frequently accessed files while maintaining overall policy compliance
Data Source
AI summary
A method, system and computer program product for data backup based on a workload behavior includes a first computer identifying, using machine learning tools, data associated with frequently accessed files on the first computer by a workload being processed by a second computer. The first computer processes the identified data associated with the frequently accessed files to generate a list of frequently accessed files by the workload, and compares the list of frequently accessed files to backup policy rules active on the first computer. Based on the comparison, the first computer identifies a mismatch between the list of frequently accessed files and the backup policy rules, and specifies a new backup rule to update the backup policy rules for each identified mismatch between the list of frequently accessed files and the backup policy rules.