Dispersed Data Slice Grouping for Fault-Tolerant Distributed Storage
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Solution Overview
Problem
Current distributed storage and task processing systems face challenges in efficiently managing and retrieving large amounts of data across multiple devices while ensuring data integrity and security, particularly in scenarios where data is distributed across geographically disparate locations.
Innovation Solution
A distributed computing system that employs dispersed error encoding and decoding techniques to segment and store data across multiple devices, allowing for reliable and secure storage and retrieval of data, even in the presence of device failures, through the use of a network of distributed storage and task execution units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is distributed across multiple geographically disparate devices, then data storage capacity and accessibility are improved, but data integrity and security become more difficult to ensure
Solution Approach 1:
The patent segments data into multiple slices and distributes them across different storage units. Each slice is independently stored, allowing the system to scale storage capacity by adding more units while maintaining data integrity through the segmentation structure and error correction codes.
Solution Approach 2:
The patent introduces error correction codes as an intermediary mechanism between the distributed data slices and the retrieval process. These codes enable the system to detect and correct errors that may occur during storage or transmission across geographically disparate devices, thereby ensuring data integrity.
2Reliability
If data is segmented and distributed across multiple devices, then system reliability is improved through fault tolerance, but system complexity increases
Solution Approach 1:
The patent divides data into segments with embedded error correction codes, creating a modular structure that provides fault tolerance. This segmentation approach allows the system to tolerate device failures while keeping the complexity of individual components manageable.
Solution Approach 2:
The patent employs error correction codes that change the parameters of the data representation by adding redundant information. This transformation enables the system to achieve fault tolerance through mathematical transformations rather than complex hardware redundancy.
3Reliability
If dispersed error encoding and decoding techniques are used, then data security and integrity are improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies error correction encoding in advance during the data storage process. This preliminary action ensures that security and integrity protections are already in place before data retrieval, reducing the computational burden and time required during the critical retrieval operation.
Solution Approach 2:
The patent replaces complex mechanical or procedural security verification mechanisms with mathematical error correction codes. This substitution enables automated, efficient processing of security checks during data retrieval, reducing processing time while maintaining strong data security.
Data Source
AI summary
A method begins with a computing device dividing data into data partitions. For a data partition of the data partitions, the method continues with the computing device associating indexing information with the data partition. The method continues with the computing device segmenting the data partition into a plurality of data segments. The method continues with the computing device dispersed storage error encoding the plurality of data segments to produce a plurality of sets of encoded data slices. The method continues with the computing device grouping encoded data slices of the plurality of sets of encoded data slices to produce a set of groupings of encoded data slices.