Geographically Diverse Storage Data Convolution
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Solution Overview
Problem
Conventional data storage techniques face challenges in efficiently managing and recovering data across geographically diverse storage zones, particularly in terms of storage space conservation, data redundancy, and disaster recovery, as they often require significant storage space and complex data replication processes.
Innovation Solution
The implementation of data convolution and deconvolution techniques, such as XOR operations, allows for the compression of data into smaller chunks that can be stored and recovered across different geographic locations, reducing storage space requirements and facilitating efficient data redundancy and recovery through the creation of convolved and deconvolved data chunks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is replicated across geographically diverse storage zones, then data recovery capability is improved, but storage space consumption increases
Solution Approach 1:
The patent combines multiple data chunks from different storage zones using convolution operations (e.g., XOR) to create compressed representations. Instead of storing full replicas of data in each geographic zone, the system merges data from multiple zones into compact convolved forms that consume less storage space while preserving the ability to recover original data through deconvolution operations.
Solution Approach 2:
The patent transforms data from its original state into a different parameter state through convolution operations. Data chunks are mathematically transformed into convolved representations that occupy less storage space but contain equivalent information content, allowing recovery when sufficient convolved chunks are available from different geographic zones.
2Quantity of substance
If data is convolved and compressed to reduce storage space, then storage space conservation is improved, but data management complexity increases
Solution Approach 1:
The patent segments data into discrete chunks that can be independently convolved, stored, and managed across different geographic zones. This segmentation allows the system to apply convolution operations to individual chunks rather than entire data sets, simplifying the management of convolved data while achieving storage space conservation through the chunked approach.
3Reliability
If conventional data replication is used across geographic zones, then data redundancy is improved, but the replication process becomes complex and inefficient
Solution Approach 1:
The patent uses mathematical copying through convolution operations instead of physical data replication. Instead of copying entire data sets across geographic zones, the system creates convolved representations that encode information from multiple source chunks. This approach achieves data redundancy with simpler operations, as convolution is computationally more efficient than traditional replication methods.
Data Source
AI summary
Scaling out of a geographically diverse storage system is disclosed. A first chunk can be selected to be moved in response to scaling out of the geographically diverse storage system. In some embodiments, the first chunk can be convolved, combined, etc., with a second chunk prior to moving a representation of information comprised in the first chunk, e.g., via copy and delete operations. The moving can be in accord with a geographically diverse storage system schema and can be based on a criteria associated with the geographically diverse storage system. In an embodiment the convolved chunk can be copied to a new zone and, subsequently, the first chunk can be deleted from the old zone. In another embodiment the combined chunk can be retained at the old zone and the first chunk can be copied to the new zone prior to deleting the first chunk from the old zone.