Virtual Machine Backup Checkpoint Handling via History Data Validation
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Solution Overview
Problem
Handling checkpoints in virtual machine backups requires numerous steps to maintain consistency and can increase data overhead, leading to longer processing times.
Innovation Solution
A system that retrieves history data with change tracking identifiers to validate current VM configurations, determining backup types as incremental or full based on the history data, and efficiently backing up each consistent state, thereby minimizing processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If checkpoints are handled in traditional backup processes to maintain consistency, then backup reliability is improved, but processing time and data overhead increase
Solution Approach 1:
The system performs preliminary validation of history data against current VM configuration before the actual backup process. By pre-identifying consistent states and their change tracking identifiers, the system prepares the backup structure in advance, avoiding time-consuming consistency checks during the backup execution phase.
Solution Approach 2:
The backup process is segmented into distinct phases: validation phase (comparing history data with current configuration), identification phase (determining consistent states and backup types), and execution phase (performing incremental or full backups). This segmentation allows each phase to be optimized independently, reducing overall processing time while maintaining reliability.
2Reliability
If checkpoints are handled in traditional backup processes to maintain consistency, then backup reliability is improved, but device complexity increases
Solution Approach 1:
The system uses self-service mechanisms where the backup process automatically validates its own history data against current VM configurations and identifies consistent states without external intervention. The change tracking identifiers serve as self-identifying markers that enable the system to autonomously determine backup types and maintain consistency, reducing the need for complex external management.
Solution Approach 2:
The system implements feedback loops where backup history data is continuously validated against current VM configurations. The change tracking identifiers provide feedback information that automatically informs the backup process about which states are consistent and what type of backup is needed, simplifying the control logic while ensuring reliability.
3Productivity
If selective backup types are used based on history data validation, then productivity is improved, but measurement precision requirements increase
Solution Approach 1:
The system uses change tracking identifiers as simplified copies or representations of the actual data change states. Instead of performing complex analyses to determine what has changed, the system relies on these identifier copies to quickly identify consistent states and determine backup types, maintaining high precision while improving productivity.
Data Source
AI summary
According to one embodiment, a system in response to a request to back up a virtual machine (VM) to a backup storage system, retrieves history data from the backup storage system. The history data has been previously backed up in the backup storage system during a previous backup operation of the VM. The history data includes a change tracking identifier (ID) associated with a consistent state of a VM disk attached to the VM at the point-in-time of the previous backup operation. The system validates the history data against a current configuration of the VM. The current configuration includes information of one or more current consistent states of the VM disk. The system determines a backup type for each of the current consistent states based on the validated history data. The system backs up each of the current consistent states based on the backup type.