Backup Data Object Prioritization Using Nearest-Critical Ordering
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Solution Overview
Problem
Existing data restoration solutions lack a mechanism to determine the recovery priority of data during restore operations, leading to potential loss of critical data in case of a disaster during or before the restore operation completes.
Innovation Solution
The system reorders data objects in a nearest-critical based sequential order using modified weight-based Euclidean distances calculated between adjacent data object pairs, ensuring critical data objects are recovered first by prioritizing them based on criticality factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data objects are restored in queued order without priority sorting, then the restore operation maintains simple processing, but critical data may be lost if a disaster occurs during restoration
Solution Approach 1:
The system performs preliminary sorting of data objects by criticality factors before the restore operation begins. Criticality factors are calculated in advance based on data attributes, and data objects are pre-ordered in priority sequences. This preliminary action ensures that when restoration occurs, the system simply follows the pre-determined priority order rather than making complex decisions during the restore process itself.
Solution Approach 2:
The restore operation is segmented into priority-based batches or sequences. Data objects are divided into groups based on their criticality levels, with critical data objects restored in the first sequence, non-critical data objects restored in subsequent sequences. This segmentation allows the system to focus resources on recovering the most important data first while maintaining manageable complexity through structured organization.
2Loss of information
If critical data objects are prioritized for recovery, then data loss is minimized during disasters, but the restore operation requires complex sorting and evaluation mechanisms
Solution Approach 1:
The system changes the parameter of data object ordering from simple queue position to criticality-based priority. By introducing criticality factors as a new parameter for sorting, the system transforms the restore operation to prioritize data based on importance rather than arrival order. This parameter change enables the system to minimize data loss by ensuring critical data is recovered first, while the complexity is managed through automated calculation of criticality factors based on predefined data attributes.
3Reliability
If all data objects are restored completely before a disaster can occur, then no data is lost, but the restore operation takes excessive time and resources
Solution Approach 1:
The system performs preliminary identification and prioritization of critical data objects before the restore operation begins. By pre-determining which data objects are most critical based on their attributes and criticality factors, the system can focus restoration efforts on these high-priority objects first. This preliminary action reduces the overall restore time by ensuring that the most important data is recovered in the initial phase, rather than waiting to restore all data completely.
Solution Approach 2:
The system implements partial restoration by recovering only the most critical data objects first rather than attempting to restore all data objects simultaneously. This partial action approach ensures that essential data is recovered within a shorter time frame, while less critical data can be restored subsequently or with lower priority. This strategy balances data recovery completeness with time constraints by accepting that not all data will be restored to the same level of completeness simultaneously.
Data Source
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AI summary
A method and system for prioritizing critical data object recovery during restore operations. Specifically, the method and system disclosed herein entail reordering data objects, awaiting being read from backup storage and thus queued in one or more data object queues, in accordance with a nearest-critical based sequential order. The nearest-critical based sequential order may be derived through modified weight-based Euclidean distances calculated between adjacent data object pairs queued in any given data object queue. Further, the calculated modified weight-based Euclidean distances incorporate data criticality factors associated with the adjacent data object pairs. By reordering data objects in a nearest-critical based sequential order, critical data objects may be recovered first, thereby avoiding possible critical data loss should a disaster occur during restore operations.