Dispersed Storage Network Data Slice Rebuilding
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Solution Overview
Problem
Existing storage networks face challenges in efficiently rebuilding encoded data slices that are corrupted or missing, which can lead to data loss and require redundant copies.
Innovation Solution
A dispersed storage network (DSN) with a rebuilding module that detects missing or corrupted data slices, retrieves a decode threshold number of good slices, and uses error encoding functions like Cauchy Reed-Solomon to rebuild the slices, ensuring data integrity and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If redundant copies of data are stored in the storage network, then data availability and integrity are improved, but storage space requirements and system complexity increase
Solution Approach 1:
The patent segments data into smaller encoded slices that are distributed across multiple storage units. Instead of storing complete redundant copies, the data is divided into K slices where only K-1 slices need to be retrieved and reconstructed, reducing the storage overhead while maintaining data availability.
Solution Approach 2:
The patent uses error correction codes to create encoded slices that can be reconstructed from fewer copies than traditional redundancy methods. The encoded slices are distributed across storage units, and the system can retrieve and reconstruct data from a subset of these slices, reducing the need for full redundant copies.
2Reliability
If the storage network rebuilds corrupted or missing data slices, then data integrity is improved, but processing time and network bandwidth consumption increase
Solution Approach 1:
The patent applies partial action by retrieving only the necessary number of encoded slices (at least K-1 slices) needed for reconstruction rather than retrieving all slices or using full redundancy. This partial retrieval approach reduces network bandwidth consumption and processing time while still achieving data integrity through successful reconstruction.
3Reliability
If the storage network uses error encoding functions like Cauchy Reed-Solomon, then data reconstruction capability is improved, but computational complexity of encoding and decoding increases
Solution Approach 1:
The patent changes parameters such as the number of encoded slices K and the minimum slices needed for reconstruction (K-1) to optimize the balance between data reconstruction capability and computational complexity. By adjusting these parameters, the system achieves sufficient reliability without excessive computational overhead.
Data Source
AI summary
A storage network operates by: encoding, via a dispersed error encoding, at least one data object as a write threshold number of encoded data slices; storing the write threshold number of the encoded data slices in a set of storage units of the storage network; determining when, due to a failure, less than a pillar width number of the encoded data slices are retrievable from the set of storage units: retrieving a decode threshold number of the encoded data slices from others of the set of storage units; decoding the decode threshold number of the encoded data slices to reproduce the at least one data object; restoring availability of the pillar width number of the encoded data slices of the common revision by: rebuilding at least a subset of encoded data slices of the set of encoded data slices, based on an encoding of the at least one data object; and storing the at least a subset of encoded data slices in the storage network.