VM Image Metadata Indexing via Extractive Catalog
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Solution Overview
Problem
In virtual machine (VM) backups, obtaining file metadata efficiently is challenging due to limited CPU and IO throughput in virtualization environments, as existing solutions either incur high network bandwidth costs, require large disk space, and additional CPU resources, or complicate user experience with agent deployment and synchronization.
Innovation Solution
An index library is used to extract metadata from consistent VM states, which are then stored in a metadata catalog and downloaded by an index server, reducing IO traffic and balancing resources between storage and indexing systems, allowing for efficient indexing of VM images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the index server downloads and parses VM backups to obtain file metadata, then file level metadata can be obtained for search, but network bandwidth cost increases and large disk space is required
Solution Approach 1:
The patent extracts only the necessary file metadata information from VM backups using an index library, rather than downloading and parsing entire VM disk data. The index library selectively extracts metadata such as file names, paths, and timestamps, significantly reducing network bandwidth consumption while maintaining search capability.
Solution Approach 2:
The index library performs metadata extraction in advance during the backup process, creating a metadata catalog before the search operation occurs. This preliminary action eliminates the need to parse full VM backups at search time, reducing both network bandwidth usage and disk space requirements for the index server.
2Loss of information
If the index server downloads and parses VM backups to obtain file metadata, then file level metadata can be obtained for search, but additional CPU resources are required
Solution Approach 1:
The index library extracts only essential metadata fields from VM backups rather than performing full parsing of disk data. This selective extraction approach minimizes CPU resource consumption by processing only the necessary information elements needed for search operations.
Solution Approach 2:
Metadata extraction is performed in advance during backup operations, distributing the CPU workload over time rather than concentrating it during search operations. This preliminary processing reduces peak CPU requirements for the index server.
3Loss of information
If an agent is deployed on each VM client to collect file metadata, then file level metadata can be obtained, but deployment complexity increases and user experience deteriorates
Solution Approach 1:
The patent introduces an index library as an intermediary component that operates on the backup server side rather than requiring client-side agents. This intermediary extracts metadata from backup data during the backup process, eliminating deployment complexity while maintaining metadata collection capability.
Solution Approach 2:
The backup system itself performs metadata extraction through the index library during the backup process, without requiring separate agent software on client machines. This self-service approach leverages existing backup infrastructure to provide metadata collection, simplifying deployment and improving user experience.
Data Source
AI summary
According to one embodiment, one or more consistent states of a virtual machine (VM) are backed up and stored. An index library is used to extract, from the consistent state(s) of the VM, metadata corresponding to content files captured in the consistent state(s). The extracted metadata is stored into a metadata catalog, where the metadata catalog is subsequently downloaded by an index server to index the content files captured in the consistent state(s).