Lock-Free Erasure Coding Across Clustered Data Stores
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Solution Overview
Problem
Existing erasure coding methods struggle to maintain data consistency and correctness across multiple data stores, especially during constant updates and device failures, leading to potential data loss and incorrect reconstruction.
Innovation Solution
A distributed clustering method with a lock-free technique enables multiple active processes to function collectively for erasure encoding and decoding across dispersed data stores, using a sequencing method to ensure data consistency and correctness, and facilitates self-healing and recovery from failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If algebraic-based coding is applied to generate redundant data blocks, then data reliability is improved, but device complexity increases due to the need to ensure all redundant blocks are completely updated
Solution Approach 1:
The system segments the encoding process by having each data store independently encode its data blocks using algebraic-based coding to generate redundant blocks. This segmentation allows parallel processing across multiple data stores, improving reliability while distributing the computational complexity across multiple independent units rather than requiring centralized coordination of all redundant block updates.
Solution Approach 2:
The system performs preliminary actions by pre-generating redundant data blocks using algebraic-based coding before actual data storage operations. This preliminary encoding ensures that redundant blocks are ready in advance, improving data reliability while reducing the complexity of real-time update coordination, as the encoding relationships are established beforehand.
2Reliability
If re-encoding process is executed to regenerate redundant data blocks, then data consistency is improved, but productivity decreases due to repeated encoding operations
Solution Approach 1:
The system implements feedback mechanisms where data stores monitor the consistency status of their data blocks and redundant blocks. When inconsistencies are detected, only the affected data stores re-encode their specific blocks rather than triggering system-wide re-encoding. This feedback-driven approach maintains data consistency while minimizing unnecessary encoding operations, thus preserving productivity.
Solution Approach 2:
Instead of re-encoding all data blocks systematically, the system applies partial action by re-encoding only the specific data blocks that are identified as inconsistent through monitoring. This selective re-encoding approach maintains data consistency where needed while avoiding the productivity loss associated with comprehensive re-encoding of all blocks.
3Manufacturing precision
If locking mechanisms are used to ensure data consistency during encoding, then data correctness is improved, but speed decreases due to process blocking
Solution Approach 1:
Each data store independently performs encoding operations on its own data blocks without requiring locks from other data stores. The system achieves data correctness through self-service encoding where each unit autonomously generates its redundant blocks using algebraic-based coding, eliminating the need for locking mechanisms and maintaining high encoding speed while ensuring correctness.
Solution Approach 2:
The system performs preliminary encoding actions where each data store pre-generates its redundant blocks before other data stores operate. This preliminary action establishes the encoding relationships in advance, allowing subsequent operations to proceed without locking while maintaining data correctness through the pre-established encoding structure.
4Reliability
If systematic re-encoding of all data blocks is performed, then data consistency is improved, but loss of time increases due to extensive processing
Solution Approach 1:
The system uses feedback mechanisms to identify only those data blocks that require re-encoding based on consistency monitoring. This feedback-driven approach prevents systematic re-encoding of all blocks, reducing the time loss while maintaining data consistency by targeting only the necessary blocks for re-encoding operations.
Solution Approach 2:
The system applies partial action by re-encoding only the specific data blocks that are identified as inconsistent rather than performing systematic re-encoding of all blocks. This selective approach maintains data consistency where needed while significantly reducing the time loss associated with comprehensive re-encoding operations.
Data Source
AI summary
The present invention provides a distributed clustering method to allow multiple active instances of consistency management processes that apply the same encoding scheme to be cooperative and function collectively. The techniques described herein facilitate an efficient method to apply an erasure encoding and decoding scheme across dispersed data stores that receive constant updates. The technique can be applied on many forms of distributed persistent data stores to provide failure resiliency and to maintain data consistency and correctness.