Data Stream Coding Across Distributed Storage Slices
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Solution Overview
Problem
Current data storage systems face challenges with data integrity and security due to the failure of memory devices, particularly those using physical movement technologies, leading to bit-level corruption and increased maintenance needs, as well as inefficiencies and security risks associated with redundant array of independent discs (RAID) solutions.
Innovation Solution
A distributed storage network that employs error coding dispersal storage functions to partition data into slices, which are then encoded and stored across multiple physically diverse locations, allowing for reliable and secure data retrieval even in the event of device failures, using techniques like Reed-Solomon encoding and parity data to ensure data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If memory devices using physical movement technologies are used for data storage, then storage capacity and accessibility are improved, but data integrity deteriorates due to bit-level corruption from device failures
Solution Approach 1:
The patent segments data into multiple slices and distributes them across different storage devices. Each slice is a portion of the original data, and the complete data can be reconstructed from any sufficient combination of slices. This segmentation approach allows the system to tolerate device failures while maintaining data integrity, as not all slices need to be present simultaneously for data recovery.
Solution Approach 2:
The patent applies error correction encoding to data slices before storing them. This preliminary encoding adds redundant information that enables the system to detect and correct bit-level corruption that occurs during storage. By performing this protective action in advance, the system ensures data integrity even when physical storage devices fail or corrupt data over time.
2Reliability
If redundant array of independent discs (RAID) solutions are implemented, then data redundancy and reliability are improved, but security risks and operational overhead increase
Solution Approach 1:
The patent divides data into slices and distributes them across storage devices, creating redundancy without requiring complex RAID configurations. Any sufficient combination of slices can reconstruct the original data, providing flexibility and simplifying the storage architecture compared to traditional RAID systems that require specific device configurations and maintenance procedures.
Solution Approach 2:
The patent changes the approach to redundancy from physical device replication (RAID) to mathematical encoding relationships. By using error correction codes and slice distribution algorithms, the system achieves data redundancy through parameter transformations rather than physical duplication, reducing operational overhead and security risks associated with traditional RAID implementations.
3Productivity
If data is stored in centralized locations, then access efficiency is improved, but security against unauthorized access and single points of failure deteriorates
Solution Approach 1:
The patent segments data into slices and distributes them across multiple storage devices located in different physical locations. This distribution eliminates single points of failure while maintaining access efficiency through parallel retrieval operations. The system can reconstruct data from any sufficient combination of slices, providing both security through distribution and efficiency through parallel processing capabilities.
Data Source
AI summary
A method begins by a processing module concurrently receiving a first data stream and a second data stream for transmission to a receiving entity. The method continues with the processing module dividing each of the first and second data streams to produce a first plurality of data blocks corresponding to the first data stream and a second plurality of data blocks corresponding to the second data stream, where data blocks of the first plurality of data blocks are time aligned with data blocks of the second plurality of data blocks. The method continues with the processing module creating a data matrix from the first and second plurality of data blocks and generating a coded matrix from the data matrix and an encoding matrix. The method continues with the processing module outputting a plurality of pairs of coded values of the coded matrix to the receiving entity.