Priority-Based Erasure Coding for Archived Data Reliability
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Solution Overview
Problem
Current data storage methods are inefficient and resource-intensive, lacking flexibility in evaluating and prioritizing data storage order, which hampers the reliability of archived data.
Innovation Solution
A priority-based method that generates importance levels for data objects, determines reliability urgency levels for storage devices, calculates erasure encoding rates, and computes parity objects on demand using an erasure code algorithm to enhance data storage reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data storage methods are used, then data is stored without prioritization, but the reliability of archived data is insufficient and the process is resource-intensive
Solution Approach 1:
The system changes the parameter of data prioritization by introducing importance levels (e.g., critical, important, standard) and applying different erasure encoding rates based on these levels. This allows the system to optimize reliability for critical data while maintaining reasonable efficiency for standard data, resolving the contradiction between reliability and productivity
Solution Approach 2:
The patent applies local quality by treating different data objects differently based on their importance level. Each data object receives customized protection (erasure encoding rate) according to its specific needs rather than applying a uniform approach, thereby improving overall system efficiency while ensuring critical data has high reliability
2Reliability
If comprehensive data storage evaluation is performed, then storage decisions can be made, but the process is complicated and time-consuming
Solution Approach 1:
The system performs partial evaluation by focusing only on the most relevant factors (importance level, health factor) rather than comprehensively evaluating all possible data attributes. This selective approach reduces evaluation time while still achieving reliable storage decisions for archived data
3Ease of manufacture
If uniform erasure encoding is applied to all data, then implementation is simple, but resource consumption is high and flexibility is low
Solution Approach 1:
The system changes the erasure encoding rate parameter based on data importance levels. Critical data receives higher encoding rates (e.g., 2x or 3x) for maximum reliability, while standard data uses lower rates, thereby reducing overall resource consumption while maintaining implementation feasibility through automated classification
Data Source
AI summary
A method and system for determining priority is provided. The method includes generating a list defining specified data objects stored within a back-up/archived data storage system and applying importance levels to the specified data objects. Reliability urgency levels for the storage devices are determined and in response groups of data objects of the specified data objects are generated. Required reliability levels for each group of data objects are determined and associated erasure encoding rates are calculated. Fragment sets for the groups of data objects are generated and numbers of parity objects required for the fragment sets are determined. An erasure code algorithm is executed with respect to the groups of data objects and in response parity objects are computed on demand.