Intelligent Data Protection Platform for Multi-Tenancy Storage
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Solution Overview
Problem
Current data management systems in distributed environments face inefficiencies in resource utilization due to the lack of effective data classification and storage pool selection, leading to suboptimal storage and backup performance.
Innovation Solution
A method and system that utilize data classification, machine learning, and pattern recognition to determine the appropriate data protection pool for storing data, and monitor data to migrate it to optimal pools based on changing service level agreements and data characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored in a single storage pool without classification, then storage simplicity is maintained, but storage efficiency and resource utilization deteriorate
Solution Approach 1:
The storage system is segmented into multiple storage pools with different service levels (e.g., high-performance SSD pool, standard HDD pool, archival tape pool). Data is automatically routed to appropriate pools based on classification tags, improving storage efficiency while maintaining manageable complexity through automated policies.
Solution Approach 2:
An intelligent data protection platform acts as an intermediary between data sources and storage pools. This platform performs data classification, determines appropriate storage pools based on service level agreements, and manages data movement, thereby improving storage efficiency without requiring direct complex management at the data source level.
2Measurement precision
If manual data classification and storage pool selection is performed, then storage accuracy is improved, but operational time and resource consumption increase
Solution Approach 1:
The system implements self-service automated data classification using machine learning models that automatically analyze data characteristics, assign classification tags, and determine optimal storage pools without human intervention. This maintains high classification accuracy while eliminating manual operation time.
Solution Approach 2:
Data classification and storage pool selection are performed in advance as data is ingested, rather than during retrieval or management operations. The system pre-tags data with classification information and pre-determines optimal storage locations, ensuring accurate classification without adding operational delays during critical operations.
3Quantity of substance
If computing resources are utilized for data backup operations, then backup capacity is improved, but overall system performance deteriorates
Solution Approach 1:
Different storage pools provide different service levels tailored to specific data requirements. Critical business data is stored in high-performance pools with dedicated resources, while archival data uses lower-performance pools. This local optimization ensures that backup capacity is maximized without uniformly degrading system performance across all operations.
Solution Approach 2:
Backup workloads are segmented and distributed across multiple storage pools with different performance characteristics. The system can simultaneously perform high-speed backups to SSD pools for critical data while conducting slower backups to HDD or tape pools for archival data, thereby increasing total backup capacity without uniformly impacting system performance.
4Productivity
If data is migrated between storage pools, then storage optimization is improved, but system complexity and operation time increase
Solution Approach 1:
The system continuously monitors data characteristics, access patterns, and storage pool performance, using this feedback to automatically trigger data migrations when optimization opportunities arise. Machine learning models predict future data needs and proactively migrate data before performance degradation occurs, optimizing storage without requiring complex manual intervention.
Solution Approach 2:
The intelligent data protection platform serves as an intermediary that abstracts the complexity of data migration from the underlying storage system. It manages pool selection, data classification, and migration orchestration through automated policies, thereby achieving storage optimization while hiding management complexity from users and simplifying operations.
Data Source
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Figure 1B
Figure 2A
AI summary
A method for managing data includes identifying, in response to a storage request from a tenant system, a first data protection pool based on a data classification analysis and initiating storage of data associated with the storage request in a first storage system associated with the first data protection pool.