File Categorization for Incremental Snapshot Efficiency
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Solution Overview
Problem
Conventional file management systems are inefficient when generating incremental data snapshots for large filesets, such as a typical operating system's root filesystem, as they require processing a large number of files, many of which remain unchanged, leading to wasteful scanning and backup processes.
Innovation Solution
Classifying files into discontiguous filesets based on factors like frequency of change or purpose, allowing for differential processing and snapshot frequency, where files are grouped and managed according to their association rather than their location in a directory hierarchy, enabling more targeted and efficient data management operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional incremental data snapshot process is used to scan all files in a large fileset, then complete data protection is ensured, but scanning time and resources increase significantly
Solution Approach 1:
The patent divides the fileset into multiple categories based on file characteristics such as change frequency, file type, or access patterns. This segmentation allows the backup system to process different categories with different strategies, reducing the time spent scanning unchanged files while maintaining protection for critical files.
Solution Approach 2:
The patent applies different backup strategies to different file categories. Files with high change frequency or critical importance receive more intensive monitoring and frequent snapshots, while files with low change frequency receive less intensive processing. This local differentiation optimizes the balance between protection completeness and scanning efficiency.
2Measurement precision
If traditional scan phase processes all files in the fileset, then all changed files are detected, but processing efficiency decreases for large filesets with many unchanged files
Solution Approach 1:
The patent segments the fileset into categories and applies selective scanning based on category characteristics. By pre-categorizing files based on their likelihood of changing, the system can focus detailed scanning on categories more likely to have changes while using lighter processing for stable categories, maintaining detection accuracy while improving efficiency.
Solution Approach 2:
The patent applies partial scanning action to certain file categories rather than scanning all files uniformly. For categories with low change probability, the system uses simplified detection methods or skips detailed scanning, while reserving thorough scanning for categories with high change probability, thus achieving sufficient detection accuracy without the full cost of scanning every file.
Data Source
AI summary
According to various embodiments, with respect to a target set of files being managed (e.g., protected by data snapshots), each file in the target set of files is classified into one of two or more filesets (discontiguous filesets), where each of these filesets comprises one or more files that are related to each other by one or more factors, such as frequency of file change or purpose of existence (e.g., used by a software application). Once classified, files within the target set of files can be uniquely processed by a data management operation (e.g., incremental data snapshot process) according to their association to a discontiguous fileset.