Storage Node Selection Using Encoded Data Preferences
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Solution Overview
Problem
Current distributed storage systems face challenges in efficiently managing and retrieving large amounts of data across geographically dispersed locations, particularly in ensuring data integrity and availability while handling complex tasks, due to limitations in error correction and data partitioning strategies.
Innovation Solution
A distributed computing system that employs dispersed error encoding and decoding techniques, where data is segmented, encoded, and distributed across multiple execution units, allowing for reliable storage and retrieval of data and execution of tasks through pillar and slice groupings, ensuring data integrity and availability even with failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is distributed across multiple geographically dispersed locations, then data availability and reliability 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 execution units in a dispersed storage network. Each slice is encoded with error correction information, allowing the system to maintain data integrity while distributing data across multiple locations. The segmentation principle is applied through slice grouping and pillar formation where data is divided into manageable units that can be independently stored and retrieved.
Solution Approach 2:
The patent introduces an intermediary encoding layer that processes data before distribution. Error correction codes and slice groupings act as intermediaries that protect data integrity during transmission and storage across dispersed locations. The encoding mechanism mediates between the original data and its distributed representation, ensuring that even if some slices are lost or corrupted, the original data can be reconstructed.
2Reliability
If error correction encoding is applied to distributed data, then data integrity is improved, but processing complexity and computational overhead increase
Solution Approach 1:
The error correction process is segmented into manageable stages: slicing data into smaller units, grouping slices into pillars, and applying encoding at the pillar level. This segmentation reduces the computational complexity of error correction by breaking down large data sets into smaller, independently processable units that can be encoded and decoded more efficiently.
Solution Approach 2:
The patent applies error correction encoding selectively rather than to all data uniformly. Slice groupings allow the system to apply error correction only where needed, and the degree of error correction can be adjusted based on the importance and access patterns of different data slices. This partial application of error correction reduces overall processing complexity while maintaining data integrity for critical data.
3Quantity of substance
If data is partitioned and distributed across multiple execution units, then storage scalability is improved, but data retrieval and task execution efficiency decrease
Solution Approach 1:
The patent performs preliminary actions by pre-grouping data slices into pillars and pre-encoding them with error correction information before distribution. This preliminary organization allows the system to retrieve data more efficiently because the grouping structure is already in place, eliminating the need for complex real-time reorganization during retrieval operations. Task execution units can directly access pre-grouped slices without additional processing overhead.
4Reliability
If redundant copies of data are stored, then data availability is improved, but storage space consumption increases
Solution Approach 1:
The patent uses error correction encoding as a form of intelligent copying rather than simple redundancy. Instead of storing multiple identical copies of the entire data set, the system creates encoded versions of data slices that can be combined to reconstruct the original data. This copying mechanism provides data availability similar to redundancy but consumes significantly less storage space because the encoded slices share information efficiently.
Solution Approach 2:
The patent changes the parameter representation of data from raw storage to encoded storage. By transforming data into error correction encoded form, the system achieves better space efficiency while maintaining availability. The encoded representation allows the system to recover original data from fewer physical storage units compared to traditional redundancy approaches, effectively changing the storage density parameter.
Data Source
AI summary
Methods and apparatus for selection of memory devices in a distributed storage network. In an example, a computing device receives a data object for storage and selects a set of storage nodes of a plurality of sets of storage nodes for storing the data object. Selection of the set of storage nodes includes determining storage attributes associated with each set of storage nodes of the plurality of sets of storage nodes. Selection of the set of storage nodes additionally includes determining a storage preference associated with the data object, and comparing the storage preference with the storage attributes of the plurality of sets of storage nodes to determine a best match. Following selection of a set of storage nodes, the computing device facilitates storage of the data object in the selected set of storage nodes.