Dispersed Storage Pool Management for Data Integrity
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Solution Overview
Problem
Current dispersed storage networks face challenges in efficiently managing data retention and distributed task processing, particularly in ensuring data integrity and security across geographically diverse storage units, and in handling large volumes of data and complex tasks.
Innovation Solution
A distributed computing system that employs dispersed error encoding and decoding schemes, allowing data to be segmented, encoded, and distributed across multiple storage units, with error correction mechanisms to ensure data integrity and security, and a task processing framework that partitions tasks and executes them in a distributed manner across multiple execution units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is distributed across multiple geographically diverse storage units, then data availability and reliability are improved, but data integrity and security management become more difficult
Solution Approach 1:
The patent segments data into multiple slices and distributes them across different storage pools, implementing error correction coding to maintain integrity. Each slice is independently stored with redundancy information, allowing the system to recover from failures while maintaining data security across geographically diverse locations.
Solution Approach 2:
The patent introduces pool managers as intermediary components that coordinate between storage units and client systems. These pool managers handle error correction, slice management, and security protocols, simplifying the complexity of managing distributed data integrity across multiple locations.
2Reliability
If data is segmented and encoded across multiple storage units, then data security and fault tolerance are improved, but storage and retrieval time increase
Solution Approach 1:
The patent performs preliminary error correction encoding and slice distribution when data is written to storage pools. This preliminary action prepares the data structure in advance, enabling faster retrieval by pre-computing redundancy information and organizing slices for efficient reconstruction, reducing the time penalty during read operations.
3Reliability
If complex error correction schemes are implemented, then data integrity is improved, but computational overhead and system complexity increase
Solution Approach 1:
The error correction mechanism is segmented into modular components distributed across pool managers and storage units. Each unit implements localized error correction logic rather than centralized complex processing, reducing overall system complexity while maintaining data integrity through distributed computational effort.
4Productivity
If large volumes of data are processed in a distributed manner, then processing capacity and availability are improved, but task coordination and execution complexity increase
Solution Approach 1:
Tasks are segmented into smaller sub-tasks that can be independently executed by different storage pools. The system divides large data processing tasks into manageable units distributed across multiple pools, with each pool handling its portion autonomously, thereby increasing processing capacity while reducing coordination complexity through task decomposition.
Data Source
AI summary
A method begins by a dispersed storage (DS) processing module selecting storage pools within the DSN with available capacity for storing data of a storage group. The method continues by selecting one or more dispersed storage (DS) units within each of the selected storage pools based on a selection criteria and mapping the one or more DS units to the storage group. The method continues by receiving a write request to store a data object to the storage group and by storing the data object in at least one of the mapped one or more DS units. The method continues with the DS processing module issuing an indication unutilized storage space calculated on a proportionate basis based on storage utilized for the storage group as a percentage of total storage utilized and updating a write proportion value based on received storage utilization responses.