Access-Level Data Storage Using Dispersed Slices for Reliability
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Solution Overview
Problem
Existing data storage and processing systems face challenges in efficiently managing large volumes of data across distributed networks, particularly in terms of storage reliability, security, and efficient task processing, while maintaining data integrity and reducing the need for redundant copies.
Innovation Solution
A distributed computing system that employs dispersed storage and task processing units, utilizing error encoding and decoding techniques to store and process data across geographically diverse locations, ensuring data integrity and security, and enabling efficient task execution through coordinated management of vaults and execution units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored using traditional redundant copy methods, then data reliability is improved, but storage efficiency deteriorates due to increased storage requirements
Solution Approach 1:
The patent segments data into multiple slices that are distributed across different storage locations. Instead of storing complete redundant copies, the system divides data into portions and disperses them, allowing reconstruction from any sufficient subset of slices. This segmentation approach maintains reliability while reducing total storage requirements compared to full redundancy.
Solution Approach 2:
The system dynamically adjusts storage parameters including the number of slices, replication factor, and access levels based on data characteristics and access patterns. By changing these parameters, the system optimizes the balance between reliability and storage efficiency for different data types and access requirements.
2Productivity
If data is frequently accessed and updated, then operational efficiency is improved, but storage consistency deteriorates due to synchronization challenges
Solution Approach 1:
The patent implements dynamic access levels that can change based on data age, access patterns, and update frequency. Hot data receives higher access levels for faster retrieval, while cold data transitions to lower access levels. This dynamic adjustment optimizes operational efficiency for frequently accessed data while maintaining storage consistency through coordinated updates.
Solution Approach 2:
The system monitors data access patterns and update frequencies, using this feedback to adjust access levels and storage strategies. This feedback mechanism ensures that storage consistency is maintained by identifying and prioritizing updates to frequently accessed data while reducing synchronization overhead for less critical data.
3Speed
If access levels are increased for faster data retrieval, then retrieval speed is improved, but system complexity deteriorates due to multi-level management
Solution Approach 1:
The patent applies different access levels locally to different data slices based on their specific access requirements. Instead of uniformly managing all data at the same access level, the system assigns appropriate access levels to individual slices or data groups, optimizing retrieval speed for critical data while simplifying management for less critical data.
4Quantity of substance
If data is compressed to reduce storage space, then storage efficiency is improved, but data integrity deteriorates due to compression errors
Solution Approach 1:
The system incorporates error correction codes and redundancy mechanisms before data compression to cushion against potential compression errors. By preparing protective measures in advance, the system can detect and correct errors that may occur during compression and decompression, maintaining data integrity while achieving storage space reduction.
Data Source
AI summary
A computing device is operable to obtain a data segment for storage via a storage network and obtain access level information regarding the data segment. The data segment is stored in memory of the storage network in accordance with a storage approach, wherein the storage approach is based on the access level information regarding the data segment.