Dispersed Storage Network Data Recovery via Error Encoding
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Solution Overview
Problem
Current dispersed storage networks face challenges in reliably recovering stored data due to individual storage device failures and network equipment failures, requiring redundant copies and being prone to data loss and hacking attempts.
Innovation Solution
A distributed computing system that uses dispersed error encoding to store data across multiple geographically disparate sites, allowing for reliable and secure data retrieval even in the presence of failures, by encoding data into encoded slices that can be reconstructed from any subset meeting a decode threshold, and employing a distributed task processing network for secure and efficient data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If redundant copies of data are stored to ensure data recovery, then data reliability is improved, but storage space efficiency deteriorates
Solution Approach 1:
The patent segments data into multiple slices and distributes them across different storage devices using dispersed error encoding. Instead of storing complete redundant copies, the system divides data into portions that can be reassembled from any sufficient subset, achieving reliability without full duplication
Solution Approach 2:
The system changes the parameter of data representation by encoding data into encoded slices with specific redundancy properties. The encoded slices are designed so that anydecode threshold number of slices can reconstruct the original data, optimizing the balance between reliability and storage efficiency
2Reliability
If data is stored across multiple geographically disparate sites, then fault tolerance is improved, but system complexity deteriorates
Solution Approach 1:
The patent creates a universal encoding system that works across any number of geographically disparate sites. The dispersed error encoding scheme provides a unified approach that handles distribution, redundancy, and recovery consistently across different locations, managing complexity through standardization
Solution Approach 2:
The system creates encoded copies of data slices that can be distributed across multiple sites. These encoded slices are mathematical transformations of the original data that maintain the ability to reconstruct the original information, enabling geographic distribution without proportionally increasing system complexity
3Object-affected harmful factors
If dispersed error encoding is used to store data across multiple sites, then data security is improved, but data retrieval complexity deteriorates
Solution Approach 1:
The patent replaces mechanical copying and manual retrieval processes with mathematical encoding and decoding operations. The dispersed error encoding uses algebraic structures to automatically handle security and retrieval, substituting complex manual procedures with systematic mathematical operations that are more manageable
Data Source
AI summary
A method begins by a processing module of a dispersed storage network (DSN) determining a fault domain for a portion of the DSN and generating a local redundancy for the fault domain. The method continues with the processing module identifying storage locations available for storing the first local redundancy, selecting storage locations for storing the first local redundancy and continues with the processing module facilitating storage of the local redundancy in the storage locations. Upon detecting a storage failure in the DSN, the method continues with the processing module determining whether the storage failure is associated with the fault domain and in response determining whether the first local redundancy is associated with the first fault domain. In response to determining that the local redundancy is associated with the first fault domain the method continues with the processing module recovering the local redundancy and correcting the storage failure.