Distributed Storage Task Execution with Encoded Partial Results
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Solution Overview
Problem
Current distributed storage and task processing systems face challenges in efficiently managing and processing large volumes of data across multiple devices, particularly in ensuring data integrity and security, and in effectively distributing tasks across a network to enhance processing efficiency.
Innovation Solution
A distributed computing system that employs dispersed error encoding and decoding, allowing data to be segmented, encoded, and distributed across multiple storage units, with a network of execution units that can perform tasks on these encoded data slices, ensuring data integrity and security through error correction and secure processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is distributed across multiple storage units without redundant copies, then storage efficiency is improved, but data reliability deteriorates
Solution Approach 1:
The patent segments data into multiple slices and distributes them across different storage units. Each slice is independently stored, allowing the system to store more data across the same infrastructure without requiring complete redundant copies, thereby improving storage efficiency while maintaining reliability through distributed redundancy.
Solution Approach 2:
The patent transforms data from its original form into encoded representations using error correction codes. This parameter change in data representation allows the system to achieve both storage efficiency and reliability, as the encoded data can be reconstructed from fewer than the total number of slices, enabling efficient storage without complete redundancy.
2Productivity
If tasks are distributed across multiple execution units, then processing efficiency is improved, but system complexity worsens
Solution Approach 1:
The patent creates execution units that can handle multiple types of tasks including data processing, error correction, and reconstruction operations. This multi-functionality allows the system to improve processing efficiency across different operation types without proportionally increasing system complexity, as the same infrastructure supports diverse computational needs.
Solution Approach 2:
The patent introduces coordination mechanisms that act as intermediaries between distributed execution units and the central control system. These intermediaries manage task distribution, result aggregation, and error handling, thereby enabling efficient distributed processing while abstracting away the complexity from the overall system architecture.
3Reliability
If error correction is implemented in distributed storage, then data integrity is improved, but computational overhead worsens
Solution Approach 1:
The patent applies error correction encoding to data slices during the initial storage phase rather than performing complex error correction computations during retrieval. This preliminary action ensures data integrity is built into the stored representation, reducing computational overhead during data access operations while maintaining high reliability.
Solution Approach 2:
The patent uses efficient copying mechanisms where error correction information is integrated into the data slice structure itself rather than requiring separate correction data. This approach improves data integrity through built-in error correction while minimizing computational overhead by avoiding redundant copying and processing of correction information.
Data Source
AI summary
A method begins by a dispersed storage (DS) processing module receiving a partial task regarding an encoded data block grouping. The method continues with the DS processing module performing the partial task on the encoded data block grouping to produce a partial task result and determining subsequent treatment of the partial task result. When the subsequent treatment includes storage of the partial task result, the method continues with the DS processing module determining a manner in which the partial task result is to be stored. When the manner in which the partial task result is to be stored is dispersed storage, the method continues with the DS processing module dispersed storage error encoding the partial task result to produce one or more sets of encoded partial task result blocks and outputting the one or more sets of encoded partial task result blocks to a set of DST execution units.