Backup Lineage Sampling for Faster Corruption Detection

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Solution Overview

Problem

Existing data protection systems face challenges in ensuring that backups are not infected with malware, as testing backups before restoration is costly and time-consuming.

Innovation Solution

Implementing a corruption detection system that uses a machine learning model to evaluate backups, combined with a sample frequency approach to reduce read-intensive workload, allowing efficient scanning of backups while maintaining detection efficacy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If backups are tested before restoration to ensure they are not infected with malware, then the reliability of data recovery is improved, but the time and cost required for testing increases significantly

Engineering Contradiction:

Improvebackup integrityVSAvoidtesting time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent applies partial action by scanning only a sample frequency of backups rather than testing every backup before restoration. The system identifies and scans a subset of backups based on corruption likelihood, achieving adequate detection without the time cost of comprehensive testing of all backups

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent performs preliminary corruption scanning on sampled backups before the actual restoration process. By detecting corruptions in advance on a sample basis, the system can prevent restoration of corrupted backups while avoiding the time penalty of full pre-restoration testing

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If every backup is scanned for corruption before restoration, then the detection precision is improved, but the read-intensive workload and processing time worsens

Engineering Contradiction:

Improvecorruption detection accuracyVSAvoidbackup processing throughput

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system performs corruption scanning on only a sample frequency of backups rather than all backups. This partial scanning approach maintains adequate detection precision for identifying corrupted backups while significantly reducing the read-intensive workload and improving processing throughput

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent changes the parameter of scanning frequency from 100% of backups to a sampled subset. By adjusting this parameter, the system optimizes the balance between detection precision and processing throughput, reducing workload while maintaining effective corruption detection

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12481641B2Method for lineage sampling to efficiently detect corruptions

Publication Date: 2025.11.25 DELL PROD LP

AI summary

Corruption detection in backups is disclosed. Backups that are received into a backup environment are stored in corresponding lineages. A detection engine is configured to perform corruption detection operations on the most recent backups in each of the lineages based on a sample frequency. Corruption detection operations may also be performed randomly and based on unexpected or unusual changes in backup metadata.