Error Recovery Procedure Parameter Selection Using Historical Data
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Solution Overview
Problem
Current data storage systems face inefficiencies in error recovery procedures, particularly in magnetic and optical storage devices, where high error rates lead to prolonged read times and potential media damage due to inadequate perspective on drive and media behavior across larger populations, resulting in suboptimal recovery strategies.
Innovation Solution
A method for selecting error recovery procedure parameters based on historical interactions between the apparatus and multiple data storage media, as well as interactions between the data storage medium and other apparatuses, to optimize recovery processes and minimize wear on both hardware and media.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional error recovery procedure is used with limited current mount history, then the drive can make dynamic decisions within current operation, but it lacks perspective on drive and media behavior across larger populations resulting in suboptimal recovery strategies
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing error data from multiple drives and media populations before actual error recovery is needed. This historical data is stored and used to pre-determine optimal recovery strategies, so when errors occur, the drive can immediately apply proven effective recovery procedures rather than experimenting with different approaches in real-time.
Solution Approach 2:
The system implements feedback by continuously collecting error data from current and historical operations across multiple drives, analyzing this data to identify patterns and correlations, and using these insights to refine and update recovery strategies. This creates a closed-loop system where past performance informs future decisions, progressively improving recovery effectiveness.
2Reliability
If multiple ERP retries are performed to recover data with high error rates, then more data sets may be recovered, but the tape drive takes longer to read data due to multiple backhitches which degrades host performance and can further damage media
Solution Approach 1:
The system performs preliminary analysis of error patterns from historical data to predict which recovery procedures are most likely to succeed for current errors. By pre-identifying the most promising recovery strategies based on similar historical cases, the system can apply the right procedure on the first attempt rather than repeatedly trying different approaches, thereby reducing media stress and improving performance.
Solution Approach 2:
The system changes parameters by selecting specific ERP procedures based on historical error pattern analysis. Instead of uniformly applying the same recovery procedure or exhaustively trying all possible procedures, the system dynamically adjusts which recovery parameters (such as read retry count, interleaving depth, or error correction algorithms) to use based on what has been most effective for similar error conditions in the past.
3Reliability
If aggressive error recovery procedures are used to maximize data recovery, then more errors can be corrected, but the recovery process adds time, stress, and wear to both media and hardware
Solution Approach 1:
The system performs preliminary assessment of error severity and type using historical data patterns before committing to a recovery procedure. By pre-evaluating the error characteristics against historical cases, the system can determine the appropriate level of aggressive recovery needed, avoiding unnecessarily time-consuming procedures for errors that have simple solutions and applying more aggressive methods only when historically justified.
Solution Approach 2:
The system applies partial action by selecting recovery procedures matched to the actual error severity rather than always applying maximum aggressive recovery. Historical data analysis allows the system to determine when moderate recovery efforts are sufficient and when more aggressive approaches are warranted, avoiding the time and wear costs of excessive recovery actions on errors that don't require them.
Data Source
AI summary
In one general embodiment, a method for selecting parameters of an error recovery procedure includes detecting an error during performance of a data operation on a data storage medium by an apparatus. In response to detecting the error, parameters of an error recovery procedure are selected based at least in part on: (a) first information about previous interactions between the apparatus and multiple data storage media, and (b) second information about previous interactions between the data storage medium and other apparatuses.