Locally Decodable Redundancy for Storage Network Data Recovery
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Solution Overview
Problem
Existing data storage and processing systems face challenges in efficiently storing and retrieving large volumes of data across distributed networks while maintaining data integrity and security, particularly in the presence of failures and hacking attempts.
Innovation Solution
A distributed computing system utilizing dispersed storage and task processing units that employ locally decodable redundancy data encoding, enabling secure and reliable storage and retrieval of data across geographically diverse locations, with error correction and integrity verification mechanisms to handle failures and maintain data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data storage systems are used in distributed networks, then data can be stored across multiple locations, but data integrity and security cannot be maintained in the presence of failures and hacking attempts
Solution Approach 1:
The patent segments data into multiple data slices and distributes them across different storage units in the network. Each slice is further protected through encoding schemes that allow reconstruction from a subset of slices, isolating the impact of failures or attacks to specific segments rather than the entire dataset.
Solution Approach 2:
The patent employs parameter changes through various encoding schemes (e.g., Reed-Solomon, erasure coding) that transform data into redundant forms. These encoding parameters can be adjusted to provide different levels of fault tolerance and security, allowing the system to maintain data integrity under varying threat conditions.
2Reliability
If redundancy data is added to protect against failures, then data reliability improves, but storage efficiency deteriorates due to increased data volume
Solution Approach 1:
The patent implements partial redundancy by storing only the minimum necessary number of data slices required for reconstruction. Instead of duplicating entire datasets, the system stores k slices out of n total slices (where k < n), allowing recovery from any k slices. This provides fault tolerance while minimizing storage overhead.
Solution Approach 2:
The system allows dynamic adjustment of redundancy parameters such as the ratio of data slices to total slices, and the degree of encoding. These parameters can be optimized based on specific requirements for fault tolerance versus storage efficiency, enabling flexible trade-offs between reliability and storage space utilization.
3Reliability
If complex encoding schemes are used for error correction, then data security improves, but processing complexity increases
Solution Approach 1:
The patent divides complex encoding and decoding operations into smaller, manageable tasks distributed across multiple storage units and processing nodes. Each node handles local encoding/decoding of its assigned data slices, reducing the processing burden on any single device while maintaining overall security through the collective operation of the distributed system.
Solution Approach 2:
The system implements self-service through automated encoding and decoding operations that occur transparently during data storage and retrieval. The encoding schemes are applied automatically when data is written, and decoding is performed automatically during retrieval, eliminating the need for manual intervention and reducing operational complexity despite the mathematical complexity of the underlying algorithms.
Data Source
AI summary
A storage network is operable to obtain a first subset of a set of encoded data slices, where the set of encoded data slices correspond to data that is coded in accordance with dispersed error coding parameters that include a read threshold number, and where the read threshold number is a number of the set of encoded data slices that is required to decode the data. Based on the number of encoded data slices in the first subset being less than the read threshold number, at least one rebuilt encoded data slice corresponding to at least one additional encoded data slice of the set of data slices not included in the first subset of the set of encoded data slices is generated utilizing locally decodable redundancy data. The data is recovered based on the at least one rebuilt encoded data slice and the first subset.