Data Recovery Duration Prediction via Phase Metadata
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Solution Overview
Problem
Current methods lack a mechanism to predict the data recovery duration on a specific storage apparatus before the data recovery process starts, making it difficult for users and engineers to evaluate and plan the data recovery process effectively.
Innovation Solution
A method that determines a plurality of phases of data recovery, estimates the recovery duration of each phase based on relevant metadata metrics, and uses supervised learning to predict the data recovery duration by selecting suitable existing recovery records as a training dataset, allowing for accurate estimation of the entire data recovery process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data recovery is performed on a storage apparatus, then data availability is restored, but the duration of data unavailability increases
Solution Approach 1:
The system performs preliminary analysis of storage apparatus metadata and recovery characteristics before actual data recovery to predict the recovery duration. This allows users to plan and schedule recovery operations during periods of lowest impact, effectively reducing the operational disruption caused by data unavailability.
Solution Approach 2:
The storage apparatus automatically collects and stores its own recovery-related metadata and characteristics during previous recovery operations. This self-collected data is then used to predict future recovery durations, eliminating the need for external measurement systems and enabling continuous improvement of prediction accuracy.
2Measurement precision
If data recovery duration is predicted using detailed metadata analysis, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The storage apparatus pre-collects and organizes recovery-related metadata during normal operation and previous recovery processes. This preliminary data preparation includes storing recovery characteristics, storage capacity information, and other relevant parameters in a structured format, which simplifies the subsequent prediction process while maintaining high accuracy.
Solution Approach 2:
The system creates a simplified model or copy of the recovery process characteristics based on historical metadata. This model captures the essential relationships between metadata parameters and recovery duration without requiring complex real-time analysis, thereby reducing system complexity while preserving prediction accuracy.
Data Source
AI summary
Technique for determining a data recovery duration involve determining a plurality of phases of data recovery. Such techniques further involve determining, based on a metadata metric set of a phase in the plurality of phases, a recovery duration of the phase, the recovery duration of the phase representing a duration required for recovery of the phase. Such techniques further involve determining the data recovery duration based on the recovery duration of the phase. Accordingly, a data recovery duration can be accurately predicted, and a user can know in advance how long data unavailability will last, which helps the user make a decision to schedule the data recovery in an appropriate time period, thereby being more effective and efficient for the user.