Smart Data Recovery Planning With Metadata-First Restore
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Solution Overview
Problem
Restoring large amounts of heterogeneous data to a production environment after catastrophic events like disasters or ransomware attacks is complex and time-consuming, impacting productivity and recovery times.
Innovation Solution
Implementing on-demand rapid recovery techniques that utilize machine learning and artificial intelligence to restore metadata before payload data, predict recovery times, and generate a 'smart recovery planner' to optimize data recovery processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data restoration methods are used to restore large amounts of heterogeneous data, then complete data recovery is achieved, but recovery time becomes excessively long and productivity is impacted
Solution Approach 1:
The patent segments the data restoration process into distinct phases: metadata restoration and payload data restoration. By dividing the heterogeneous data into structured components (metadata containing organizational information and payload containing actual data), the system can restore metadata first to enable rapid access to data locations and structures, then restore payload data subsequently. This segmentation resolves the contradiction by making the overall recovery process manageable and time-efficient while ensuring complete data recovery.
Solution Approach 2:
The patent applies preliminary action by restoring metadata before payload data. Metadata contains critical information about data structure, organization, and location that enables the system to prepare the restoration environment in advance. By performing this preliminary restoration step first, the system establishes the framework needed for efficient subsequent payload restoration, thereby reducing total recovery time while maintaining complete data recovery.
2Reliability
If all data is restored simultaneously to ensure complete recovery, then data integrity is maintained, but system complexity and operational difficulty increase
Solution Approach 1:
The patent divides the restoration operation into separate manageable tasks: metadata restoration and payload restoration. This segmentation simplifies the operational complexity by allowing administrators to understand and control each phase independently, while still achieving complete data integrity through the coordinated execution of both phases.
Solution Approach 2:
The patent introduces dynamic prioritization where metadata restoration is performed first based on its critical importance for data organization and access. This dynamic approach to operation sequencing makes the restoration process more manageable and easier to operate, while the system maintains data integrity through the structured two-phase restoration methodology.
3Productivity
If preferred data sets are prioritized for restoration, then user experience and productivity are improved, but determining restoration priorities requires complex analysis
Solution Approach 1:
The patent incorporates feedback mechanisms that analyze data usage patterns, access frequencies, and business criticality to automatically determine restoration priorities. This feedback-driven approach enables the system to intelligently prioritize preferred data sets without requiring complex manual analysis, thereby improving restoration efficiency while keeping the priority determination process automated and manageable.
Solution Approach 2:
The system performs self-service by automatically analyzing and determining restoration priorities based on embedded metadata and usage patterns. This eliminates the need for complex external analysis and allows the system to autonomously optimize restoration sequences, improving productivity without proportionally increasing operational complexity.
4Loss of time
If metadata is restored before payload data to speed up recovery, then restoration time is reduced, but the restoration process becomes more complex
Solution Approach 1:
The patent formally structures the restoration process into two distinct segments: metadata restoration and payload restoration. This segmentation, while introducing procedural complexity, enables significant time savings by allowing metadata to be restored and made available immediately. The structured approach makes the complexity manageable through clear phase definitions and enables parallel processing opportunities.
Solution Approach 2:
By performing metadata restoration as a preliminary action before payload restoration, the system establishes the organizational framework needed for efficient data recovery. This preliminary step reduces overall restoration time significantly, and the added process complexity is justified by the substantial time savings achieved through this structured two-phase approach.
Data Source
AI summary
The present inventors devised technological improvements that substantially speed up data recovery. Disclosed techniques include on-demand rapid recovery based on restoring metadata from backups before restoring payload data according to certain preferences and priorities. Disclosed techniques further include predicting how long a restore job might take based on simulated restore operations, and using the predicted restore times to generate a “smart recovery planner” or “recovery playbook” that is designed to optimize massive data recovery after a catastrophic data loss event, such as a disaster, ransomware attack, malware infection, etc. The disclosed techniques are especially well suited to optimizing large data recoveries and improving the user experience by prioritizing preferred data sets.