Compressed Extent Versions for Online Virtual Volume Migration
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Solution Overview
Problem
Efficiently migrating or copying a virtual volume tree across storage systems while maintaining online availability is challenging, especially when multiple snapshots are involved, as existing methods often require downtime and struggle with preserving data consistency and compression efficiency.
Innovation Solution
The approach involves compressing extent collections of a virtual volume tree, which includes multiple versions of a single extent from both base and snapshot virtual volumes, to achieve high compression ratios and efficient data transfer, allowing for online migration by replicating writes and maintaining snapshot data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple snapshots are taken of a base volume to preserve data versions, then data consistency and recovery capability are improved, but storage space consumption and migration complexity increase
Solution Approach 1:
The patent segments the virtual volume tree into individual extent versions that can be independently compressed and migrated. Each extent version is processed separately, allowing the system to handle multiple snapshots without treating them as a monolithic complex structure. This segmentation enables parallel processing and reduces overall migration complexity while preserving all snapshot versions.
Solution Approach 2:
The patent merges multiple extent versions from different snapshots into a single compressed data stream during migration. By combining redundant data from multiple versions and applying compression algorithms, the system reduces the total data volume to be migrated while maintaining all necessary version information for data consistency and recovery.
2Productivity
If compression is applied to extent collections during migration, then data transfer efficiency is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary identification and grouping of extent versions before compression. By pre-organizing the data structure and identifying redundant extents across snapshots, the system prepares the data in an optimal format for compression, reducing the actual compression processing time while maximizing compression ratios.
Solution Approach 2:
The patent dynamically adjusts compression parameters based on the characteristics of the extent collection being processed. By changing compression levels, algorithms, or data representation formats adaptively, the system optimizes the balance between compression ratio and processing time for different types of data workloads.
3Ease of operation
If the source storage system remains online during migration, then system availability is improved, but data consistency and migration reliability may be compromised
Solution Approach 1:
The patent implements continuous data capture and compression during the migration process, allowing the source system to remain online and operational. The compression and migration process continues uninterrupted alongside normal write operations, with the system continuously synchronizing data from the source to the target while maintaining source availability.
Solution Approach 2:
The patent employs feedback mechanisms to monitor data consistency between source and target systems during online migration. By continuously checking for discrepancies and adjusting the migration process in real-time, the system maintains data integrity even while the source remains operational and subject to ongoing writes and changes.
Data Source
AI summary
Examples include compressed extent versions. Examples may create an empty target virtual volume tree having a tree structure of a source virtual volume tree of a source storage system, the source virtual volume tree comprising source base and source snapshot virtual volumes, each representing respective versions of a plurality of extents, wherein each of the extents corresponds to a different portion of an address space of the source base virtual volume. Examples may include compressed extent collections, each comprising a compressed representation of multiple populated versions of a single extent from the source base and snapshot virtual volumes, the multiple populated versions compressed relative to one another.