Adaptive Data Backup Retention Policy for Storage Optimization

Solution Overview

Problem

Current data backup systems face challenges in ensuring robustness due to unreliable data retention policies, particularly when systems go offline or experience corruption, leading to potential data loss during restore operations.

Innovation Solution

An adaptive data backup retention policy that adjusts pruning schedules based on reliability metrics, increasing backup frequency during periods of unreliability and maintaining a greater population of backups to ensure robust recovery options.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If data backups are pruned according to a nominal schedule, then storage space is optimized, but data recovery robustness deteriorates during unreliable periods

Engineering Contradiction:

Improvestorage spaceVSAvoiddata recovery robustness

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The retention policy dynamically adjusts the pruning schedule based on reliability metrics. During periods of system unreliability (offline periods, corruption events), the policy automatically retains more backups than the nominal schedule would dictate. This dynamic adaptation ensures that storage space is optimized during normal operation while maintaining enhanced data recovery robustness during unreliable periods.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system monitors reliability metrics and uses this feedback to adjust the retention policy. When reliability metrics indicate problematic periods (such as offline events or corruption), the system automatically modifies the pruning behavior to retain additional backups. This feedback loop ensures that the retention policy responds to actual system conditions rather than following a rigid predetermined schedule.

Inventive Principle:

Principle #23Feedback

2Reliability

If backup frequency is increased during unreliable periods, then data recovery robustness is improved, but storage space consumption increases

Engineering Contradiction:

Improvedata recovery robustnessVSAvoidstorage space

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The retention policy dynamically adjusts the pruning schedule based on reliability metrics. During periods of system unreliability (offline periods, corruption events), the policy automatically retains more backups than the nominal schedule would dictate. This dynamic adaptation ensures that storage space is optimized during normal operation while maintaining enhanced data recovery robustness during unreliable periods.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The retention policy applies different retention characteristics to different time periods based on reliability conditions. During unreliable periods, the policy locally increases retention to preserve more backups, while during reliable periods, it returns to the nominal aggressive pruning schedule. This local quality adjustment ensures that enhanced robustness is applied only where needed rather than uniformly across all time periods.

Inventive Principle:

Principle #3Local quality

3Quantity of substance

If aggressive pruning is applied to optimize storage, then storage efficiency is improved, but the risk of data loss increases during corruption events

Engineering Contradiction:

Improvestorage efficiencyVSAvoiddata loss risk

Core Design Contradiction:

Quantity of substanceVSObject-affected harmful factors

Solution Approach 1:

The retention policy prepares for potential data loss by maintaining additional backups during and around periods of unreliability. When corruption events or offline periods are detected, the policy automatically retains more backups than the nominal schedule would allow, creating a cushion of additional recovery points. This beforehand cushioning protects against the harmful effect of data loss while still allowing aggressive pruning during reliable periods.

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

Solution Approach 2:

The system monitors reliability metrics and uses this feedback to adjust the retention policy. When reliability metrics indicate problematic periods (such as offline events or corruption), the system automatically modifies the pruning behavior to retain additional backups. This feedback loop ensures that the retention policy responds to actual system conditions rather than following a rigid predetermined schedule.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS10956282B2Adaptive data retention policy based on reliability metric or metrics

Publication Date: 2021.03.23 DATTO LLC

AI summary

The disclosure provides methods and systems for adaptive data retention. According to an embodiment, data backups are acquired from a protected computing device, and stored on a backup computing device. A retention policy is applied to selectively prune the data backups stored on the backup computing device. At least one reliability metric is used to decide to continue pruning according to a nominal schedule or prune according to a less aggressive schedule.