Cloud Backup Hierarchical Systems Using Injective Representations
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Solution Overview
Problem
Existing methods for backing up hierarchical systems, such as file systems or databases, are inefficient and lack effective deduplication and version comparison capabilities, especially in cloud storage environments.
Innovation Solution
The use of injective representations and reversal information to reconstruct and update hierarchical systems in cloud blob storage, enabling efficient parallel processing, deduplication, and rapid content recovery by applying a statistically injective function to the system's content and utilizing a change journal to track changes, allowing for efficient backup and version comparison without revealing sensitive data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional backup methods are used for hierarchical systems, then backup functionality is provided, but processing speed and storage efficiency are insufficient
Solution Approach 1:
The hierarchical system is divided into multiple nodes representing different levels of the hierarchy. Each node can be processed independently and in parallel, allowing the backup operation to be segmented into concurrent tasks that can be executed simultaneously across multiple processing units, thereby dramatically improving backup processing speed.
Solution Approach 2:
Injective representations of hierarchical nodes are pre-computed and stored before actual backup operations. These pre-computed representations enable rapid identification of changed nodes during backup, allowing the system to skip unchanged portions and only backup modified data, significantly reducing backup time.
2Quantity of substance
If traditional backup methods are used for hierarchical systems, then backup functionality is provided, but deduplication capabilities are lacking
Solution Approach 1:
Instead of backing up actual hierarchical data structures, the system creates and stores injective representations (hashes) of each node. These compact representations serve as unique identifiers that enable deduplication by detecting identical nodes across different versions or locations without storing redundant copies of the actual data.
Solution Approach 2:
The system transforms hierarchical node data into a different parameter space by applying injective functions (hashing). This parameter transformation converts variable-length hierarchical data into fixed-size representations, enabling efficient comparison and deduplication while reducing storage requirements for backup metadata.
3Measurement precision
If traditional backup methods are used for hierarchical systems, then backup functionality is provided, but version comparison capabilities are insufficient
Solution Approach 1:
The system extracts only the essential identifying characteristics of hierarchical nodes by computing their injective representations. By separating the identification function (hash computation) from the actual data storage, the system enables precise change detection through simple hash comparison, avoiding the complexity of comparing entire hierarchical structures.
Solution Approach 2:
Injective representations of all hierarchical nodes are pre-computed and stored alongside the backup data. This preliminary computation of hashes allows for rapid and precise version comparison by simply comparing hash values rather than performing complex structural comparisons, significantly improving change detection accuracy while reducing comparison complexity.
Data Source
AI summary
The efficient backing up of a hierarchical system in cloud blob storage. The hierarchical structure of the system as it existed at a prior instance in time is reconstructed. A change journal that represents changes in the file system that prior instant in time is then used to formulate an updated file system hierarchy as it exists at a second instant in time. An updated injected representation of the file system, and updated file system reversal information is then formulated and provided to cloud blob storage. The injected representation of the file system is a one-to-one function of the content of the file system, in that the reversal information can be used to recover the content of the file system. Injected representations of various nodes in the system hierarchy may also be remotely stored.