Multi-Level Lightweight Snapshots for Rapid Data Restore
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Solution Overview
Problem
Current data restore techniques require frequent backups, leading to excessive storage costs and long restore times due to the need for high-frequency snapshotting, which users find unacceptable as they demand quicker restore times without incurring higher storage expenses.
Innovation Solution
Implementing multiple levels of lightweight snapshots that capture data at varying granularities, allowing for efficient storage and rapid recovery by cascading data from higher to lower levels, reducing the need for extensive storage and processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If frequent backups are taken to reduce restore time, then restore speed is improved, but storage cost increases
Solution Approach 1:
The patent divides backup data into multiple hierarchical levels (Level 0 being the most recent, Level 1, Level 2, etc.), where each level contains snapshots at different frequencies. This segmentation allows the system to provide rapid restore capability for recent data while using less frequent backups for historical data, thus reducing overall storage requirements while maintaining fast restore performance.
Solution Approach 2:
The system implements periodic snapshots at different frequencies for different levels. Level 0 snapshots are taken most frequently (e.g., every minute), while Level 1, Level 2, and subsequent levels use progressively lower frequencies. This periodic action pattern enables the system to balance restore speed requirements with storage cost constraints.
2Measurement precision
If high-frequency snapshots are taken to enable granular restore, then restore granularity is improved, but device complexity increases
Solution Approach 1:
The patent segments the snapshot hierarchy into multiple levels with different granularities. Level 0 provides fine-grained restore capability for recent data, while higher levels provide coarser granularity for historical data. This segmentation allows the system to achieve granular restore without the complexity of maintaining uniformly high-frequency snapshots across all time periods.
Solution Approach 2:
The system adds a hierarchical dimension to the snapshot structure, organizing snapshots not just by time but by level. This dimensional organization simplifies the complexity by providing a structured approach to managing granular restore capabilities across different time periods and frequency requirements.
Data Source
AI summary
Systems for high performance restore of data to storage devices. A method embodiment commences upon identifying a plurality of virtual disks to be grouped together into one or more consistency sets. Storage I/O commands for the plurality of virtual disks of the consistency sets are captured into multiple levels of backup data. On a time schedule, multiple levels of backup data for the virtual disks are cascaded by processing data from one or more higher granularity levels of backup data to one or more lower granularity levels of backup data. A restore operation can access the multiple levels of backup data to construct a restore set that is consistent to a designated point in time or to a designated state. Multiple staging areas can be maintained using lightweight snapshot data structures that each comprise a series of captured I/Os to be replayed over other datasets to generate a restore set.