Dispersed Storage Tracking Table for Data Integrity
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Solution Overview
Problem
Current dispersed storage networks face challenges in securely and reliably storing and retrieving large amounts of data across multiple geographically dispersed locations, particularly in maintaining data integrity and efficiently processing tasks distributed across these networks.
Innovation Solution
A distributed computing system that employs dispersed error encoding and decoding, where data is segmented, encoded, and distributed across multiple storage units, allowing for secure storage and retrieval while maintaining data integrity through error correction and secure task processing across geographically dispersed units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is distributed across multiple geographically dispersed storage units, then system reliability and fault tolerance are improved, but data integrity verification and retrieval complexity increase
Solution Approach 1:
The patent segments data into multiple slices that are distributed across different storage units. Each slice is independently stored with its own checksum, allowing verification without retrieving the entire dataset. This segmentation approach maintains reliability while simplifying integrity verification at each node.
Solution Approach 2:
The patent introduces checksums as intermediary values that mediate between stored data slices and verification requirements. These checksums enable efficient integrity verification without requiring complex cross-node validation, reducing the overall system complexity while maintaining high reliability.
2Reliability
If data is encoded and distributed across multiple storage units, then data security and fault tolerance are improved, but storage and retrieval time increase
Solution Approach 1:
By segmenting data into smaller slices distributed across multiple units, the system enables parallel retrieval operations. Multiple slices can be fetched simultaneously from different storage units, reducing overall retrieval time while maintaining fault tolerance through the distributed architecture.
Solution Approach 2:
The patent implements threshold-based retrieval where only a minimum number of slices (threshold) are required to reconstruct the original data. This allows the system to retrieve data faster by obtaining just enough slices rather than requiring all distributed copies, reducing retrieval time while preserving fault tolerance.
3Manufacturing precision
If checksums are generated and verified for each data slice, then data integrity is improved, but processing overhead and computational complexity increase
Solution Approach 1:
The patent extracts integrity verification into separate checksum values that are stored independently with each data slice. This extraction allows verification to occur at the slice level without requiring complex processing of the entire dataset, reducing computational overhead while maintaining high data integrity.
Solution Approach 2:
Each data slice is self-contained with its own checksum, enabling independent verification without requiring external validation from other slices or centralized processing. This self-service approach to integrity verification simplifies processing overhead while ensuring comprehensive data integrity across all distributed slices.
4Reliability
If large datasets are stored in a dispersed storage network, then storage capacity and reliability are improved, but task processing efficiency and coordination complexity increase
Solution Approach 1:
The patent segments both data and processing tasks into corresponding slices that can be processed in parallel across distributed storage units. This segmentation enables independent task execution at each node, improving overall processing efficiency while maintaining storage reliability through the distributed architecture.
Solution Approach 2:
The patent creates a universal slice structure that serves multiple functions: data storage, integrity verification (via embedded checksums), and task processing. This multi-functional slice design eliminates the need for separate coordination mechanisms, improving task processing efficiency while preserving storage reliability.
Data Source
AI summary
A method begins by a dispersed storage (DS) processing module dividing a very large data object into a plurality of data regions and generating a data object storage tracking table that includes sections for identifying one or more data regions that are available or unavailable for retrieval. The method continues with the DS processing module dividing a first data region into data segments and disperse storage error encoding the data segments to produce sets of encoded data slices. The method continues with the DS processing module sending DSN write requests regarding storing the sets of encoded data slices to storage units and when at least a write threshold number of write responses is received for each of the sets of encoded data slices, updating the data object storage tracking table to indicate that the first data region is available for retrieval.