Cloud Storage Data Segmentation for Recovery Time
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Solution Overview
Problem
Current cloud storage systems face challenges in efficiently managing data recovery, retention, compliance with regulatory requirements, and security, particularly in handling inactive data, event-based retention, sensitive data classification, and virus scanning within cloud storage.
Innovation Solution
Implementing a method that allows for selective recovery of active data, event-based retention management, inline data classification, item-level Write Once Read Many (WORM) compliance storage policies, synchronization of security access controls, and virus scanning with quarantining of infected items within cloud storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud storage systems store all data including inactive data, then data completeness is maintained, but data recovery time and costs increase significantly
Solution Approach 1:
The patent segments data into active and inactive portions, storing active data in cloud storage while maintaining inactive data locally or in archive. This segmentation allows selective recovery of only active data, dramatically reducing recovery time while preserving data completeness through the distributed storage architecture.
Solution Approach 2:
The patent extracts inactive data from the primary cloud storage system and places it in archive or local storage. This extraction eliminates unnecessary data from the recovery process, allowing rapid recovery of active data while inactive data remains accessible through alternative means if needed.
2Reliability
If cloud storage systems implement comprehensive retention management, then regulatory compliance is improved, but system complexity increases
Solution Approach 1:
The patent implements preliminary classification of data at the time of upload, assigning retention periods and compliance metadata before data is stored. This preliminary action ensures regulatory requirements are met from the outset without requiring complex ongoing management systems.
Solution Approach 2:
The patent enables data to carry its own retention and compliance metadata, allowing the storage system to automatically manage retention policies without complex external control systems. The data itself contains the information needed for compliance management.
3Reliability
If cloud storage systems perform inline data classification, then sensitive data protection is improved, but processing time increases
Solution Approach 1:
The patent performs partial classification by focusing only on identifying sensitive data types rather than comprehensive analysis of all data attributes. This partial action approach provides adequate protection for sensitive information while minimizing the processing overhead.
Data Source
AI summary
Cloud storage provides for accessible interfaces, near-instant elasticity and scalability, multi-tenancy, and metered resources within a framework of distributed resources acing to provide highly fault tolerant solutions with high data durability. However, cloud storage also has drawbacks and limitations with information uploading and how information is subsequently accessed.