Dispersed Storage Write Tuning Through Adaptive Slice Encoding
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Solution Overview
Problem
Current dispersed storage networks face challenges in securely storing and retrieving large amounts of data across geographically diverse locations while ensuring data integrity and resistance to failures and hacking attempts.
Innovation Solution
A distributed computing system that employs dispersed error encoding and decoding techniques, where data is segmented, encoded, and distributed across multiple storage units, allowing for secure storage and retrieval with error correction capabilities, and secure task processing on stored data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is distributed across multiple geographically diverse storage units, then system reliability and resistance to failures are improved, but data security and integrity 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, and the system uses slice groups that can be retrieved from any sufficient number of storage units. This segmentation approach improves reliability by ensuring that no single point of failure can compromise the entire dataset, while the structured segmentation methodology manages the complexity of distributed security.
Solution Approach 2:
The patent introduces an intermediary encoding layer that transforms original data into encoded slices before distribution. This intermediary representation ensures that individual slices do not reveal meaningful information about the original data, enhancing security. The encoding/decoding mechanism acts as a mediator that maintains data integrity and security across the distributed network without requiring complex point-to-point security arrangements.
2Reliability
If data is encoded and distributed across multiple storage units, then error correction capabilities are improved, but storage and retrieval efficiency deteriorate
Solution Approach 1:
The patent implements error correction by distributing data as slice groups where any sufficient number of slices can reconstruct the original data. Instead of requiring all encoded slices for retrieval, the system allows partial retrieval - any enough slices from the distributed set can recover the complete data. This partial action approach provides robust error correction while maintaining efficient retrieval, as the system doesn't need to access all storage units to restore data.
Solution Approach 2:
The patent uses parameter changes in the form of adjustable slice group configurations and encoding schemes. By modifying encoding parameters and slice group structures, the system can optimize the balance between error correction capability and retrieval efficiency. Different parameter configurations allow the system to adapt to varying reliability requirements while managing storage and retrieval performance.
3Quantity of substance
If large amounts of data are stored in a dispersed network, then storage capacity and scalability are improved, but ensuring data integrity and security across all locations becomes more difficult
Solution Approach 1:
The patent divides large datasets into multiple slices that are distributed across the dispersed network. Each slice is independently stored and protected, allowing the system to scale storage capacity by adding more storage units without compromising the integrity of individual data portions. The segmentation approach enables linear scalability while maintaining consistent integrity protection across all stored data.
Solution Approach 2:
The patent implements feedback mechanisms through checksums and verification protocols that accompany each slice and slice group. These feedback elements enable the system to verify data integrity during storage and retrieval operations. When data is accessed, the system can verify the integrity of retrieved slices and request retransmission if integrity checks fail, ensuring data integrity across the scalable dispersed network.
Data Source
AI summary
A method includes monitoring write processing performance while storing a plurality of sets of encoded data slices in storage units. The method includes comparing the write processing performance with a desire write performance range. When the write processing performance compares unfavorably to the desire write performance range, the method includes establishing a data partition between the data segments of the data encoded using the first dispersed storage error encoding parameters and subsequent data segments of the data; determining second dispersed storage error encoding parameters based on the unfavorable comparison between the write processing performance and the desired write performance range; encoding the subsequent data segments of the data using the second dispersed storage error encoding parameters to produce a second plurality of sets of encoded data slices; and monitoring write processing performance while storing the second plurality of sets of encoded data slices in the storage units.