Data Management Virtualization System Reducing Redundant Storage Access
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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 access operations, as they create and move multiple copies of data across various storage repositories, which is inefficient and costly.
Innovation Solution
The Data Management Virtualization System addresses this by defining business requirements through Service Level Agreements (SLAs), leveraging deduplication and compression algorithms, and abstracting physical storage resources into virtualized storage pools, reducing redundant access operations through snapshot and difference data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple point solutions are deployed to manage different stages of data lifecycle, then data management functionality is achieved, but infrastructure complexity and cost increase
Solution Approach 1:
The patent combines multiple separate data management functions (backup, replication, archiving, disaster recovery) into a single integrated data management system. This consolidation eliminates the need for multiple independent point solutions, reducing infrastructure complexity while maintaining comprehensive data management capabilities through unified control and coordination of all data lifecycle operations
Solution Approach 2:
The integrated data management system is designed to perform multiple data lifecycle functions simultaneously - backup, replication, archiving, and disaster recovery - all through a single universal platform. This multi-functional approach allows the system to adapt to various data management needs without requiring separate specialized systems for each function
2Reliability
If multiple copies of data are created and moved across storage repositories, then data protection and availability are improved, but redundant access operations increase
Solution Approach 1:
The system creates data copies (backups, replicas, archives) across multiple storage repositories to ensure data protection and availability. However, it intelligently manages these copies by tracking their relationships and using them efficiently, reducing redundant access operations by selecting appropriate source copies based on their temporal and spatial relationships
Solution Approach 2:
The system performs preliminary organization and tracking of data copies as they are created, establishing temporal and spatial relationship information. This preliminary action enables subsequent operations to efficiently select and access appropriate data copies without redundant operations, as the system already knows the optimal sources based on pre-established relationships
3Reliability
If data is copied frequently to multiple storage locations, then data availability and recovery capability are improved, but network bandwidth and storage capacity are consumed
Solution Approach 1:
The system applies different data copying and retention strategies to different data locations and time periods. Data is copied frequently to local or nearby storage for immediate recovery needs, while less frequently accessed data is archived to remote locations. This localized quality approach optimizes network bandwidth usage by concentrating frequent transfers locally while reducing long-distance data movement
Data Source
AI summary
Systems and methods are disclosed for performing a plurality of prescribed data management functions in a manner that reduces redundant access operations to primary storage, where the system includes a data management engine for performing data management functions, including at least a snapshot function and a back-up function. An electronic service level agreement (SLA) specifies a schedule for performing data management functions, where point-in-time images of data include a reference to a baseline image and difference data indicating changes at a later, specific point in time. The data management system also creates a point-in-time image of the primary storage data in response to a schedule requiring some data management functions to be performed concurrently, and communicates the difference information to secondary storage to update the back-up copy of the primary data, such that the primary storage is accessed only once for all updates to the secondary storage.