Encoded Data Slice Retrieval in Dispersed Storage Networks
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Solution Overview
Problem
Current distributed storage and task processing systems face challenges in efficiently managing and retrieving large datasets across geographically dispersed locations, particularly in ensuring data integrity and security while handling complex tasks, due to limitations in error correction and data slicing mechanisms.
Innovation Solution
A distributed computing system that employs dispersed storage error encoding and decoding techniques, segmenting data into slices and pillars, and utilizing a network of execution units to perform tasks on encoded data, ensuring data integrity and security through redundancy and secure encoding schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is segmented into slices and distributed across multiple locations, then data security and fault tolerance are improved, but system complexity increases
Solution Approach 1:
The patent segments data into multiple slices and distributes them across different storage locations. Each slice is independently stored, and the system uses encoding techniques to ensure that any sufficient combination of slices can reconstruct the original data, thereby achieving both security and fault tolerance through segmentation
Solution Approach 2:
The patent introduces encoding and decoding mechanisms as intermediaries between the data and storage locations. These intermediaries transform the data into encoded slices that can be safely distributed and later reconstructed, managing the complexity of distributed storage through standardized encoding processes
2Reliability
If error correction encoding is applied to ensure data integrity, then data security is improved, but processing time increases
Solution Approach 1:
The patent applies error correction encoding in advance during the data storage process. By pre-encoding the data into multiple slices with built-in error correction capabilities, the system ensures data integrity is established before any potential failures occur, rather than needing to correct errors after they happen
Solution Approach 2:
The patent modifies data parameters through encoding transformations, converting original data into encoded slices with different mathematical representations. This parameter transformation enables error correction capabilities while maintaining efficient processing through optimized encoding algorithms
3Reliability
If data is encoded and sliced for distributed storage, then fault tolerance is improved, but retrieval complexity increases
Solution Approach 1:
The patent creates a universal decoding mechanism that can reconstruct original data from any sufficient combination of encoded slices, regardless of which specific locations are available. This multi-functionality allows the system to handle various failure scenarios uniformly, simplifying retrieval logic despite the distributed nature of storage
Data Source
AI summary
A method includes identifying an independent data object of a plurality of independent data objects for retrieval from dispersed storage network (DSN) memory. The method further includes determining a mapping of the plurality of independent data objects into a data matrix, wherein the mapping is in accordance with the dispersed storage error encoding function. The method further includes identifying, based on the mapping, an encoded data slice of the set of encoded data slices corresponding to the independent data object. The method further includes sending a retrieval request to a storage unit of the DSN memory regarding the encoded data slice. When the encoded data slice is received, the method further includes decoding the encoding data slice in accordance with the dispersed storage error encoding function and the mapping to reproduce the independent data object.