Dispersed Storage Mapping for Reliable Encoded Data Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current distributed storage and task processing systems face challenges in efficiently managing and retrieving large datasets across geographically dispersed locations, particularly in ensuring data integrity and security while handling complex tasks, and in efficiently distributing processing loads across multiple execution units.
Innovation Solution
A distributed computing system that employs dispersed error encoding and decoding techniques to segment and distribute data across multiple storage units, allowing for secure, reliable storage and retrieval, and enables distributed task processing by partitioning tasks across multiple execution units, ensuring data integrity and security through error correction and secure encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is distributed across multiple geographically dispersed storage units, then data availability and reliability are improved, but data integrity and security become more difficult to ensure
Solution Approach 1:
The patent segments data into multiple data objects and distributes them across different storage units. Each data object can be independently stored, retrieved, and managed, allowing the system to maintain data availability while ensuring integrity through distributed architecture. The segmentation enables parallel processing and reduces the impact of failures on the entire dataset.
Solution Approach 2:
The patent introduces an intermediary mechanism that coordinates data storage, retrieval, and validation across distributed storage units. This intermediary layer ensures that data integrity checks are performed consistently and that security protocols are maintained across geographically dispersed locations, resolving the contradiction between availability and integrity.
2Productivity
If complex tasks are processed in a distributed manner across multiple execution units, then processing speed and productivity are improved, but task distribution and coordination become more complex
Solution Approach 1:
The patent segments complex tasks into smaller sub-tasks that can be independently executed by multiple execution units. This segmentation enables parallel processing, improving overall productivity while reducing the coordination complexity by breaking down monolithic task management into manageable units that can be distributed and tracked independently.
3Quantity of substance
If large datasets are stored and processed in a dispersed network, then data capacity and processing capability are improved, but system complexity and operational difficulty increase
Solution Approach 1:
The patent implements a universal data object interface that allows the same operations to be performed on data objects regardless of their physical location or storage medium. This multi-functionality simplifies operations by providing a consistent API and interaction model, making the system easier to operate despite the dispersed network architecture and large data capacity.
Data Source
AI summary
A method includes identifying an independent data object of a plurality of independent data objects for retrieval from dispersed storage network (DSN) memory. The method further includes determining a mapping of the plurality of independent data objects into a data matrix, wherein the mapping is in accordance with the dispersed storage error encoding function. The method further includes identifying, based on the mapping, an encoded data slice of the set of encoded data slices corresponding to the independent data object. The method further includes sending a retrieval request to a storage unit of the DSN memory regarding the encoded data slice. When the encoded data slice is received, the method further includes decoding the encoding data slice in accordance with the dispersed storage error encoding function and the mapping to reproduce the independent data object.