Track Quality Classifier for Magnetic Storage Retry Latency
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Solution Overview
Problem
In magnetic storage systems, data tracks that fail to be successfully decoded within limited time frames often cannot be reprocessed within track gaps, leading to inefficiencies and increased system latency.
Innovation Solution
Implementing a track quality classification system that uses specific metrics, such as the number of failed sectors or unsatisfied parity checks, to distinguish between 'good' tracks that can be reprocessed in track gaps and 'bad' tracks that cannot, allowing for timely retry operations during normal processing periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If retry operations are performed on all failed tracks, then data retrieval reliability is improved, but system latency increases and productivity decreases
Solution Approach 1:
The patent applies local quality by differentiating between 'good' tracks and 'bad' tracks based on specific metrics (number of failed sectors, sum of unsatisfied parity checks, sum of minimum mean square error). Only good tracks are selected for reprocessing in track gaps, while bad tracks are handled differently. This selective approach ensures that retry operations are applied locally to suitable candidates, improving reliability without unnecessarily increasing latency for all failed tracks.
2Reliability
If retry operations are performed on failed tracks, then data retrieval reliability is improved, but productivity decreases
Solution Approach 1:
The system evaluates specific quality metrics for each failed track and selectively reprocesses only those classified as 'good' tracks during track gaps. This local quality approach ensures that retry operations are applied only where beneficial, maintaining high data retrieval reliability while minimizing impact on overall processing throughput by not stalling for all failed tracks.
Solution Approach 2:
The patent performs preliminary classification of failed tracks using quality metrics (failed sectors, unsatisfied parity checks, mean square error) before deciding on reprocessing. This preliminary action identifies which tracks are suitable for quick reprocessing in track gaps, enabling the system to prepare and execute targeted retry operations without disrupting the overall data processing flow, thus maintaining productivity.
3Loss of time
If track reprocessing is attempted within track gaps, then system latency is reduced, but measurement precision is required to distinguish good tracks from bad tracks
Solution Approach 1:
The patent introduces specific measurable parameters to classify track quality: number of failed sectors, sum of unsatisfied parity checks, and sum of minimum mean square error. By changing from a binary failed/not-failed state to a multi-parameter quality assessment, the system can precisely distinguish between good tracks suitable for reprocessing and bad tracks that are not, enabling latency reduction through accurate classification.
Solution Approach 2:
The system uses feedback from decoding results (number of failed sectors, unsatisfied parity checks, mean square error values) to classify tracks and determine reprocessing eligibility. This feedback mechanism provides the measurement precision needed to accurately distinguish good tracks from bad tracks, enabling the system to make informed decisions about which tracks can be successfully reprocessed within track gaps, thereby reducing latency.
4Measurement precision
If multiple metrics are used for track classification, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent employs multiple parameters (failed sectors count, unsatisfied parity checks sum, mean square error sum) to comprehensively assess track quality. These parameter changes from simple binary classification enable precise differentiation between good and bad tracks. The complexity is managed by computing these metrics during the existing decoding process, integrating the classification function into the regular data flow without requiring separate complex classification hardware.
Data Source
AI summary
An apparatus includes a storage medium operable to store a number of data tracks, a read channel circuit operable to process the data tracks read from the storage medium, and a track quality classifier circuit operable to determine a track quality metric for the data tracks read from the storage medium. The track quality metric indicates whether a corresponding one of the data tracks that has failed to successfully process in the read channel circuit can be reprocessed within a track gap period.