Scoring System Prioritizes DSN Requests by Trust and Compliance
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Solution Overview
Problem
Current computer networks face challenges in ensuring data integrity and availability across distributed storage systems, particularly in handling errors and failures without redundant copies, while also managing user access and billing efficiently.
Innovation Solution
A dispersed storage network (DSN) with a managing unit and integrity processing unit that uses error encoding techniques like Cauchy Reed-Solomon encoding to distribute data across multiple storage units, allowing for error correction and secure, long-term storage without redundant copies, along with a scoring system to prioritize and manage user requests based on trust, compliance, and billing scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If error encoding techniques are used to distribute data across multiple storage units, then data integrity and availability are improved, but system complexity increases
Solution Approach 1:
The patent divides data into multiple data slices and distributes them across different storage units using error encoding techniques. Each storage unit stores a portion of the encoded data, allowing the system to maintain data integrity even when some storage units fail. This segmentation approach enables fault tolerance without requiring complete redundancy of all data copies.
Solution Approach 2:
The patent introduces a managing unit that acts as an intermediary between data requesters and storage units. This managing unit handles error correction, coordinate data retrieval, and manages the complexity of distributed storage operations. By centralizing the error correction logic in the managing unit, the system achieves data integrity while keeping individual storage units relatively simple.
2Reliability
If redundant copies of data are stored, then data availability is improved, but storage space requirements increase
Solution Approach 1:
The patent extracts the redundancy function from complete data copies and implements it through error encoding. Instead of storing full redundant copies of all data, the system uses error correction codes that allow reconstruction of data from partial information. This reduces the storage space required while maintaining availability during failures.
Solution Approach 2:
The patent changes the storage parameter from storing complete data copies to storing encoded data slices. By transforming data into encoded form and distributing only necessary slices across storage units, the system achieves fault tolerance with reduced storage requirements. The error encoding parameters allow reconstruction of original data from a subset of stored slices.
3Ease of operation
If all user requests are treated equally, then system simplicity is maintained, but user access management and billing efficiency deteriorate
Solution Approach 1:
The patent applies different quality levels of service to different users based on their priority scores. High-priority users receive faster data access and better service levels, while low-priority users receive standard service. This local differentiation allows the system to maintain operational simplicity through automated scoring while achieving efficient user access management and billing based on actual usage patterns.
Data Source
AI summary
A method begins by a computing device of a dispersed storage network (DSN) maintaining a queue of pending requests to access the DSN while new requests are added to the queue and executed requests are deleted from the queue. The method continues by the computing device determining, for each pending request in the queue, a prioritization score to produce a plurality of prioritization scores. The prioritization score is determined by determining an identity of a requestor associated with a pending request, obtaining a trust score based on the requestor's identity, and obtaining a compliance score based on the requestor's identity. The trust score indicates the requestor's level of legitimate use of the DSN and the compliance score indicates the requestor's level of compliance with DSN system requests. The method continues by the computing device executing pending requests of the queue in accordance with the plurality of prioritization scores.