Storage Unit Prioritization for Distributed Read Retrieval
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Solution Overview
Problem
Existing data storage systems in distributed networks face challenges in efficiently managing storage units for data retrieval operations, particularly in ensuring data integrity and security while handling large volumes of data and complex tasks, and in maintaining system performance under failure conditions.
Innovation Solution
A distributed storage network (DSN) system that utilizes dispersed error encoding and decoding, along with intelligent management and integrity processing units, to store and retrieve data across geographically diverse storage units, ensuring data integrity and security, and supports distributed task processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored across multiple geographically diverse storage units in a distributed network, then system reliability and fault tolerance are improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent segments data into multiple slices and distributes them across geographically diverse storage units. Each storage unit independently stores and manages its local slices, reducing coordination complexity while maintaining overall system reliability through distributed architecture
Solution Approach 2:
The patent introduces an intermediary controller that manages the coordination between storage units. This intermediary handles complex operations such as data reconstruction, integrity verification, and failure recovery, isolating complexity from individual storage units while maintaining system-wide reliability
2Reliability
If intelligent management units are implemented to handle data integrity and security, then data security and integrity are improved, but processing overhead and system complexity increase
Solution Approach 1:
The patent implements preliminary actions by pre-computing and storing integrity check values, encryption keys, and reconstruction parameters alongside data slices. When data retrieval or verification is needed, these pre-prepared information elements enable rapid integrity checking and security verification without intensive real-time processing
Solution Approach 2:
The patent enables storage units to perform self-verification of data integrity using locally stored checksums and redundancy information. Each storage unit can independently detect and report corrupted slices without requiring constant communication with central management, reducing overall processing overhead while maintaining data integrity
3Reliability
If dispersed error encoding is used to tolerate multiple failures, then data loss resistance is improved, but storage space requirements and processing complexity increase
Solution Approach 1:
The patent employs error correction coding schemes where data slices are encoded with redundancy parameters that can be dynamically adjusted. By optimizing the redundancy ratio based on failure probability and storage capacity, the system achieves high failure tolerance while minimizing additional storage space requirements
Solution Approach 2:
The patent implements partial redundancy by storing error correction information for only the most critical data slices or using asymmetric redundancy levels across different data partitions. This approach provides sufficient failure tolerance for acceptable scenarios while reducing overall storage space consumption compared to full redundancy
4Productivity
If data retrieval operations are prioritized based on task importance, then system productivity is improved, but scheduling complexity and decision-making overhead increase
Solution Approach 1:
The patent assigns different priority levels and quality-of-service parameters to different data slices and storage units based on their importance and performance characteristics. Critical data slices are routed to high-priority storage units with faster retrieval capabilities, while less critical data uses standard retrieval paths, optimizing overall productivity without uniform complexity across the system
Data Source
AI summary
Methods and systems for use in a storage network to prioritize storage units for data retrieval operations. In various examples, a device obtains resource utilization information and pending resource demand information for a plurality of storage units of a storage network. The device further groups, based on the resource utilization information, the storage units into an underutilized resource group and an overutilized resource group, and further issues one or more high priority read slice access requests to the storage units of the underutilized resource group. The read slice access requests correspond to a pending resource demand for data retrieval. In response to determining that the high priority read access information does not include at least a read threshold number of read slice requests, the device further issues one or more additional read slice requests to one or more storage units of the overutilized resource group.