Distributed Storage Version Metadata for Fault-Tolerant Data Slices
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Solution Overview
Problem
Current distributed storage and task processing systems face challenges in efficiently managing data storage and task execution across multiple nodes, particularly in ensuring data integrity and availability in the face of node failures.
Innovation Solution
The system employs a dispersed storage network (DSN) with error encoding and decoding mechanisms to distribute data across multiple nodes, ensuring redundancy and fault tolerance. Additionally, it utilizes a distributed task processing framework that partitions tasks and data into manageable slices, which are then processed in parallel across multiple execution units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is distributed across multiple nodes in a dispersed storage network, then fault tolerance and data availability are improved, but system complexity and difficulty of managing data integrity increase
Solution Approach 1:
The patent segments data into multiple slices and distributes them across different storage units in the dispersed storage network. Each slice is independently stored, allowing the system to tolerate failures of individual storage units while maintaining overall data availability. This segmentation approach directly improves fault tolerance without requiring complex centralized management of entire data sets.
Solution Approach 2:
The patent introduces a gateway as an intermediary component that manages encoding and decoding operations between the distributed storage units and external systems. The gateway handles the complexity of data reconstruction, error correction, and coordination, shielding the distributed storage architecture's complexity from external systems while maintaining simplified access interfaces.
2Productivity
If tasks are partitioned and processed in parallel across multiple execution units, then processing speed and productivity are improved, but coordination overhead and system complexity increase
Solution Approach 1:
The patent partitions tasks into sub-tasks and distributes them across multiple execution units for parallel processing. Each execution unit independently processes its assigned sub-tasks, enabling simultaneous computation across the network. This segmentation allows the system to achieve high processing speeds by utilizing multiple nodes concurrently without requiring complex inter-unit communication during execution.
Solution Approach 2:
The patent performs task partitioning and data distribution in advance before execution begins. The gateway pre-processes tasks, divides them into manageable sub-tasks, and assigns them to appropriate execution units beforehand. This preliminary organization eliminates the need for complex real-time coordination during task execution, reducing overhead while maintaining parallel processing efficiency.
3Loss of energy
If storage units are dynamically shut down to conserve resources, then energy efficiency is improved, but data access time and system responsiveness may worsen
Solution Approach 1:
The patent implements dynamic power management where storage units can be selectively activated or deactivated based on current system workload and data access patterns. The gateway monitors usage patterns and transitions idle storage units to low-power states while maintaining readiness to activate them when needed. This dynamic approach optimizes energy consumption while minimizing impact on data access performance through intelligent resource allocation.
Data Source
AI summary
A method for execution in a storage network begins by determining to adjust a version number associated with a data object stored in the storage network, where the data object was dispersed storage error encoded to produce a set of encoded data slices. The method continues by identifying a number of versions associated with the data object and based on the number of versions identified and the adjusted version number associated with the data object, generating metadata associated with the data object. The method then continues by issuing a write request for each encoded data slice of the set of encoded data slices to the set of storage units, where each write request includes the metadata associated with the data object.