Dispersed Storage Network Data Binding and Encoding
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Solution Overview
Problem
Current dispersed storage networks face challenges in efficiently storing and retrieving large amounts of data across geographically diverse locations while ensuring data integrity and security, particularly in the presence of failures and hacking attempts.
Innovation Solution
A distributed computing system that employs dispersed error encoding and decoding, where data is segmented, encoded, and distributed across multiple storage units, allowing for reliable storage and retrieval with error correction capabilities and secure encryption, and task processing is distributed across multiple execution units for efficient processing of complex functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is distributed across multiple geographically diverse storage units, then system reliability and tolerance to failures improve, but system complexity and difficulty of managing data integrity increase
Solution Approach 1:
The patent segments data into multiple slices that are distributed across different storage units. Each slice is independently stored, allowing the system to tolerate failures of individual storage units while maintaining overall data availability. This segmentation approach directly addresses the contradiction by improving reliability through distribution while managing complexity through standardized slice structures.
Solution Approach 2:
The patent introduces an intermediary encoding layer that transforms data into encoded slices before distribution. This intermediary process manages the complexity of distributed storage by providing a systematic method for data transformation, error correction, and reconstruction, thereby reducing the management burden on the distributed system.
2Reliability
If data is encoded and distributed across multiple storage units, then data security and integrity improve, but storage and retrieval time increase
Solution Approach 1:
The patent performs preliminary encoding of data into multiple slices with error correction codes before distribution to storage units. This preliminary action ensures that data integrity is built-in from the start, allowing for faster retrieval since the encoded structure enables efficient reconstruction without requiring complex verification procedures during read operations.
Solution Approach 2:
The patent implements error correction codes that add redundant information beyond the minimum required for simple duplication. This excessive encoding provides robust data integrity protection while enabling efficient retrieval, as the redundant information allows for error correction without requiring retrieval from all storage units.
3Productivity
If complex task processing is distributed across multiple execution units, then processing efficiency improves, but coordination overhead and system complexity increase
Solution Approach 1:
The patent segments complex tasks into smaller sub-tasks that can be independently executed by different execution units. This segmentation improves processing efficiency by enabling parallel execution while reducing coordination overhead, as each execution unit operates on independent data slices with clearly defined boundaries and relationships.
Data Source
AI summary
A method includes determining, by a first client and a second client module, that a first data object and a second data objects are related. The method further includes dispersed storage error encoding the first data object to produce a first plurality of sets of encoded data slices and the second data object to produce a second plurality of sets of encoded data slices. The method further includes sending, by the first client module, first requests regarding the first plurality of sets of encoded data slices to a set of storage units. The method further includes sending, by the second client module, second requests regarding the second plurality of sets of encoded data slices to the set of storage units. The method further includes processing, by a binding module, remaining phases of the first and second write requests as a single write operation for the first and second data objects.