Erasure-Coded Data Convolution for Damaged Chunk Recovery
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Solution Overview
Problem
Conventional data storage techniques face challenges in efficiently recovering data from geographically diverse storage systems, particularly when chunks are compromised, as they often rely solely on deconvolution or erasure decoding, which may not suffice for complete data recovery in scenarios with extensive damage.
Innovation Solution
The integration of mixed deconvolution and erasure decoding techniques, allowing for the recovery of compromised data by first deconvolving and then decoding, or decoding and then deconvolving, depending on the extent of damage, to maximize fragment recovery within a geographically diverse data storage system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only deconvolution or erasure decoding is used for data recovery, then the recovery process is simple, but complete data recovery cannot be achieved when extensive damage occurs
Solution Approach 1:
The patent combines deconvolution and erasure decoding into a unified hybrid recovery framework. The system dynamically integrates both techniques, allowing deconvolution to handle certain types of damage while erasure decoding addresses others, achieving complete data recovery when either method alone would fail.
Solution Approach 2:
The recovery system dynamically selects and switches between deconvolution and erasure decoding based on the type and extent of damage detected. This dynamic adaptation allows the system to optimize recovery effectiveness for different damage scenarios while maintaining operational simplicity through automated decision-making.
2Reliability
If multiple recovery techniques are integrated, then data recovery completeness improves, but the recovery process complexity increases
Solution Approach 1:
The hybrid recovery system automatically detects the type and extent of damage, then self-selects the appropriate recovery technique (deconvolution, erasure decoding, or both) without requiring user intervention. This self-service capability maintains operational simplicity while leveraging multiple recovery methods for complete data restoration.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor recovery progress and damage characteristics, then adjust the recovery strategy in real-time. This feedback loop ensures that the complex multi-technique approach is automatically optimized based on actual system state, maintaining ease of operation through intelligent automation.
Data Source
AI summary
Data convolution for geographically diverse storage is disclosed. Data and corresponding convolutions of data can employ erasure coding to improve robustness of access to information represented in the data. For a peer group of chunks employing a given erasure coding scheme, access to the information represented in the data can be via accessible chunks and/or recovery of a less-accessible chunk, e.g., via a deconvolution operation, via a decoding operation, via a mix of deconvolution and decoding operations. The mix of deconvolution and decoding operations can enable recovery of a less-accessible chunk that cannot be recovered by either a deconvolution or decoding operation alone. This can improve access to information represented in less-available data.