Data Management Virtualization System for Deduplication
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Solution Overview
Problem
Current data management systems require multiple point solutions for managing the lifecycle of application data, leading to complex and expensive infrastructures with redundant data copies and inefficient data movement across storage repositories.
Innovation Solution
The Data Management Virtualization System leverages temporal relationships among data copies to deduplicate data, using a unified engine that manages data protection across various storage repositories, employing deduplication and compression algorithms, and abstracting physical storage resources into virtualized pools to optimize data movement and storage capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple point solutions are deployed to manage data lifecycle, then data protection and management functions are achieved, but infrastructure complexity and cost increase
Solution Approach 1:
The patent merges multiple data management functions (backup, replication, archiving, compliance) into a single unified data management system. The system consolidates what were previously separate point solutions into one integrated platform that handles all data lifecycle operations, thereby reducing infrastructure complexity while maintaining comprehensive data protection capabilities.
Solution Approach 2:
The unified data management system performs multiple functions simultaneously - it creates backups, replicates data, manages compliance retention, and handles archiving all through a single system. This multi-functional approach eliminates the need for separate specialized systems for each data management task, directly reducing complexity while achieving reliable data protection.
2Reliability
If multiple copies of data are created and moved to individual storage repositories, then data protection requirements are met, but storage capacity and network bandwidth are consumed inefficiently
Solution Approach 1:
The system creates efficient copies of data by using deduplication technology. Instead of creating completely separate copies of data for backup, replication, and archiving, the system identifies and reuses identical data blocks across different copies. This dramatically reduces the total storage capacity required while still maintaining multiple protective copies of data.
Solution Approach 2:
The system recovers and reuses data that would otherwise be redundant. Through deduplication, identical data blocks are detected across different copies and only one master copy is stored, while references to the same data are shared across multiple copies. This recovers storage capacity that would have been wasted on redundant data.
3Reliability
If data is copied frequently for backup and replication, then data protection and availability are improved, but redundant data movement operations increase
Solution Approach 1:
The system uses intelligent copying mechanisms that transfer only the differences (deltas) between data versions rather than copying entire data sets. When data changes, only the changed portions are copied and applied to existing copies, significantly reducing the amount of data movement required for backup and replication operations while maintaining data availability.
Solution Approach 2:
The system performs preliminary deduplication and difference calculation before data movement operations. By analyzing what needs to be copied in advance and identifying redundant portions, the system prepares efficient copy operations that minimize actual data transfer, thereby improving productivity while maintaining protection requirements.
Data Source
AI summary
Systems and methods are disclosed for managing deduplicated images of data objects that change over time. The method includes: organizing unique content of each data object as a plurality of content segments and storing the content segments in a data store; for each data object, creating an organized arrangement of hash structures, wherein each structure, for a subset of the hash structures, includes a hash signature for a corresponding content segment and is associated with a reference to the corresponding content segment, and for each data object, maintaining an organized arrangement of temporal structures to represent a corresponding data object over time, wherein each structure is associated with a temporal state of the data object, and wherein each temporal state is associated with the hash structures representing the content of the data object during that temporal state.