Dispersed Storage Network Address Generation Scoring
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Solution Overview
Problem
Current dispersed storage networks face challenges in reliably and securely storing and retrieving large amounts of data across multiple geographically distributed locations, particularly in maintaining data integrity and handling failures without the need for redundant copies.
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 reliable storage and retrieval even in the presence of failures, using a network of geographically dispersed storage and task execution units that can execute tasks on stored data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is distributed across multiple geographically dispersed storage units, then reliability and fault tolerance are improved, but device complexity and coordination overhead increase
Solution Approach 1:
The patent segments data into multiple encoded slices distributed across different storage units. Each slice is a independent portion of the encoded data, allowing the system to tolerate failures of individual storage units while maintaining overall data integrity. This segmentation resolves the contradiction by distributing data physically (improving reliability) while managing complexity through standardized encoding protocols.
Solution Approach 2:
The patent introduces encoding/decoding functions as intermediaries between the distributed storage units and the data retrieval process. These intermediary functions manage the complexity of coordinating multiple storage units by providing standardized interfaces for data reconstruction, thus improving reliability through distribution while containing system complexity through abstraction.
2Reliability
If redundant copies of data are stored, then reliability is improved, but storage capacity requirements increase
Solution Approach 1:
The patent applies parameter changes by using error correction coding parameters (such as Reed-Solomon codes) to transform the storage problem. Instead of storing simple redundant copies, the system encodes data with specific redundancy parameters that allow reconstruction from fewer than total stored slices, thus improving fault tolerance while optimizing storage capacity utilization through mathematical encoding relationships.
Data Source
AI summary
A method begins by a processing module of a dispersed storage network (DSN) receiving a DSN retrieval request regarding a data object and performing a scoring function using properties of the DSN retrieval request and properties of DSN memory of the DSN to produce a storage scoring resultant. The method continues with the processing module identifying a set of primary storage units based on the storage scoring resultant and sending a set of retrieval requests to the set of primary storage units. When a primary storage unit does not provide a favorable response, using the storage scoring resultant to identify an alternative storage unit. When the alternative storage unit is identified, sending a corresponding retrieval request to the alternative storage unit.