Cloud Backup Consistency Groups for Granular Data Recovery
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Solution Overview
Problem
Cloud-based platforms lack automated mechanisms for establishing consistent data protection schemes across tenancies or regions and fail to manage backups effectively, particularly in recovering data at granular levels and disposing of stale data.
Innovation Solution
A method and system for managing backups within cloud-computing environments using consistency groups, which involves identifying cloud computing resources, generating consistency groups, scheduling snapshots, indexing snapshots, and providing user interfaces for recovery options, enabling data recovery at various granularities and automating the disposal of stale data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual definition of consistency group volumes is used, then user control over data protection is maintained, but automation and efficiency are reduced
Solution Approach 1:
The system performs self-service by automatically discovering volumes, computing instances, and dependencies without requiring manual user input. The backup service autonomously identifies resources that should be included in consistency groups and establishes backup schedules based on automated resource discovery and dependency analysis.
Solution Approach 2:
The system changes the parameter of volume identification from manual specification to automated discovery based on resource tags, metadata, and dependency relationships. This transformation enables the system to dynamically identify and group volumes that require consistent backup without user intervention.
2Ease of operation
If entire volumes are mounted for data retrieval, then data recovery is simplified, but storage efficiency and granularity are reduced
Solution Approach 1:
The system segments the volume into individual files and directories, allowing selective recovery of only the specific data elements that are needed. Instead of mounting the entire volume, the backup service identifies and recovers individual files or directories at the desired granularity level, reducing storage waste and improving efficiency.
Solution Approach 2:
The system transitions from volume-level recovery to file-level and directory-level recovery by introducing a new dimension of granularity. This enables users to recover specific files or folders without mounting the entire volume, providing both simplicity and storage efficiency simultaneously.
3Loss of substance
If no automated backup management is implemented, then system complexity is reduced, but stale data accumulation and storage inefficiency increase
Solution Approach 1:
The backup service implements feedback mechanisms by continuously monitoring backup status, identifying stale backups, and automatically managing their disposal. The system provides feedback loops that track backup freshness, detect outdated data, and trigger automated cleanup processes to maintain storage efficiency without requiring manual intervention.
Solution Approach 2:
The backup management system performs self-service by automatically identifying, evaluating, and disposing of stale backups without user involvement. The system autonomously manages the backup lifecycle including creation, retention, and deletion of outdated backups, reducing storage waste while maintaining manageable complexity through automation.
4Productivity
If manual consistency group management is used, then data protection accuracy is maintained, but time consumption and productivity are reduced
Solution Approach 1:
The system performs preliminary actions by automatically discovering and categorizing volumes, computing instances, and their dependencies before backup establishment. This preliminary resource discovery and dependency mapping ensures that consistency groups are formed with appropriate volumes while maintaining data protection accuracy, eliminating the need for manual verification.
Solution Approach 2:
The system replaces the mechanical manual process of consistency group management with an automated computational system that uses resource metadata, tags, and dependency analysis to form consistent groups. This substitution maintains data protection reliability through algorithmic consistency checks while dramatically improving productivity and speed.
Data Source
AI summary
Techniques discussed herein relate to improved data recovery techniques within cloud computing environments. The disclosed techniques utilize consistency groups that are identified for volumes corresponding to a compute instance. A plurality of resource identifiers uniquely identifying a respective cloud computing resource of the cloud computing environment can be maintained. A cloud computing instance corresponding to a resource identifier may be identified and volume metadata associated with that resource identifier is obtained. The volume metadata identifying at least one of a block volume or boot volume. A consistency group is generated for the identified volume devices for the computing instance and one or more schedules can be generated with which various snapshots of the volume devices of the group are to be subsequently generated.