Remote Archive Tiering With Data Lock for Cloud Object Retention
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Solution Overview
Problem
Data management in cloud storage systems faces challenges due to accidental deletion or corruption of archived data caused by malicious attacks or human error, especially in deduplicated environments, leading to data integrity issues and unrecoverable objects.
Innovation Solution
Implementing data lock properties and expiration dates for objects, extending retention and data lock periods to prevent accidental or intentional deletion, and using tiering policies to optimize storage classes based on access and reference patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is archived to cloud storage with deduplication, then storage efficiency is improved, but data integrity and recoverability deteriorate due to accidental or malicious deletion
Solution Approach 1:
The system performs preliminary actions by creating immutable snapshots and locking data chunks before they can be deleted or corrupted. The snapshot mechanism captures the state of data at a specific point in time and prevents subsequent modifications, while the lock mechanism blocks deletion operations during the retention period. This preliminary protection ensures data recoverability even in deduplicated storage environments where space optimization might otherwise lead to data loss.
Solution Approach 2:
The system implements beforehand cushioning through retention policies that maintain copies of data chunks for specified periods. These retention periods act as a cushion against accidental or malicious deletions, ensuring that even if data is deleted from the source system or corrupted, recovered copies remain available in the cloud storage for the duration of the retention period.
2Ease of operation
If manual management of archived data is allowed, then user control is improved, but data loss risk increases due to malicious attacks or human error
Solution Approach 1:
The system applies preliminary anti-action by implementing lock mechanisms that prevent deletion operations during retention periods. Even when users have manual management capabilities, the lock mechanism blocks harmful deletion actions, whether caused by malicious attacks or human error. This creates a protective layer that counters potential data loss before it can occur.
Solution Approach 2:
The system introduces an intermediary mechanism between user control and data deletion. The retention policy and lock mechanism act as intermediaries that mediate between user requests and actual data operations. Users can manage data within the constraints of retention policies, but cannot delete locked data chunks, thus preventing data loss while maintaining operational control.
3Reliability
If data lock periods and retention policies are extended, then data protection is improved, but storage costs and resource usage increase
Solution Approach 1:
The system implements dynamic retention policies where data lock periods and retention durations can be adjusted based on data characteristics, access patterns, and organizational requirements. Rather than applying uniform extended retention to all data, the system allows flexible configuration of retention periods, enabling optimization between data protection and storage costs. Data that requires higher protection can have longer retention, while less critical data can have shorter retention periods.
Solution Approach 2:
The system applies different retention and lock policies to different data chunks or data sets based on their specific requirements. This local quality approach allows critical data to receive extended protection with longer retention periods, while non-critical data can have shorter retention periods, optimizing the balance between data protection and storage costs on a per-data-basis rather than uniformly across all data.
4Device complexity
If uniform storage class is used for all archived data, then management simplicity is improved, but storage costs increase and performance is suboptimal
Solution Approach 1:
The system implements dynamic storage class assignment that automatically moves data between different storage classes based on access patterns, age, and other criteria. Data that is frequently accessed remains in high-performance storage classes, while data that has not been accessed for extended periods is automatically moved to lower-cost storage classes. This dynamic approach optimizes both performance and costs without requiring complex manual management.
Solution Approach 2:
The system employs self-service mechanisms where the storage management system automatically determines and executes storage class transitions based on predefined policies. The system monitors data access patterns and autonomously moves data between storage classes without requiring manual intervention, thereby achieving cost optimization and performance tuning while maintaining management simplicity.
Data Source
AI summary
It is determined that an expiration date for an object associated with a first archive that includes a chunk object that includes a plurality of data chunks has expired. The first archive is stored in a first cloud performance storage class according to an archive tiering policy. It is determined that the archive tiering policy indicates migrating the plurality of data chunks included in the chunk object from the first cloud performance storage class to a second cloud performance storage class. In response to determining that the archive tiering policy indicates migrating the plurality of data chunks included in the chunk object from the first cloud performance storage class to the second cloud performance storage class, the plurality of data chunks included in the chunk object are migrated from the first cloud performance storage class to the second cloud performance storage class.