Edge Backup Prioritization for Resource-Limited Distributed Systems
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Solution Overview
Problem
Distributed systems face challenges in managing resource availability for data backups, leading to potential limitations in providing computer-implemented services due to computational expense and reduced restoration capabilities.
Innovation Solution
A method for managing distributed systems by estimating the relative importance of data portions based on access frequency and entity weights, generating data backup schedules, and allocating resources accordingly to prioritize higher importance data backups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data backups are performed frequently and comprehensively, then restoration capabilities are improved, but computational expense increases and resource availability is reduced
Solution Approach 1:
The patent applies local quality by differentiating backup priorities across different data portions based on their importance to business operations. Critical data receives frequent backups while less critical data receives less frequent backups, optimizing the balance between restoration capability and computational expense.
Solution Approach 2:
The system dynamically changes backup parameters (frequency, retention period) based on data importance classification. This allows the system to adjust backup intensity for different data portions, reducing overall computational expense while maintaining adequate restoration capabilities for critical data.
2Reliability
If resources are allocated to data backups, then restoration capabilities are improved, but service performance may be reduced due to resource constraints
Solution Approach 1:
Resources are allocated locally to different data portions based on their business criticality. High-importance data receives more backup resources while low-importance data receives fewer resources, ensuring service performance is maintained while still providing restoration capability for critical operations.
Solution Approach 2:
The system performs partial backup actions by selecting only the most critical data portions for frequent backups rather than backing up all data equally. This partial action approach ensures adequate restoration capability for essential services while minimizing impact on overall service performance.
3Reliability
If all data portions are backed up with equal priority, then comprehensive restoration is achieved, but resource efficiency is reduced
Solution Approach 1:
The patent implements local quality by assigning different backup priorities to different data portions based on their importance to business operations. This differentiated approach achieves comprehensive restoration for critical data while avoiding waste of resources on non-critical data.
Solution Approach 2:
The data portions are segmented into different importance categories, and backup resources are allocated accordingly. This segmentation allows the system to achieve comprehensive restoration capability for essential data while improving resource efficiency by reducing backups for less important data.
Data Source
AI summary
Methods and systems for managing systems are disclosed. To manage the systems, backups of data used by the systems may be generated and stored for future use. To limit overhead for generation and storage of the backups, portions of data may be evaluated for relative importance using an automated evaluation process. The automated evaluation process may take into account entities involved in data access chains, access frequency, data location, and/or other factors. The resulting evaluations may be used to selectively deploy limited computing resources for backing up of different portions of data.