Remote Agent Backup Prioritization via Asset Map Analysis
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Solution Overview
Problem
Existing data protection systems in distributed computing environments face challenges in prioritizing data backups effectively, leading to potential loss of important data due to limited computational resources and the inability to efficiently distribute workload for determining data importance.
Innovation Solution
A method is introduced that utilizes remote agents to orchestrate data protection services by generating lifecycle policies based on virtual machine asset maps, prioritizing backup generation across multiple components of the distributed system, thereby minimizing computing resource consumption and ensuring consistent data protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data backup prioritization is performed centrally without workload distribution, then data protection reliability is improved, but computational resource consumption increases and system scalability deteriorates
Solution Approach 1:
The patent segments the centralized backup management functionality into distributed components. Each computing device is assigned a backup priority score calculated from its own data characteristics (data type, size, access frequency) and system-wide factors (network conditions, overall system state). This segmentation allows local decision-making while maintaining coordinated backup prioritization across the distributed system, reducing central computational load while preserving data protection reliability.
2Ease of operation
If all data is treated equally for backup purposes, then simplicity of operation is maintained, but loss of important data increases due to inability to prioritize critical information
Solution Approach 1:
The patent implements local quality by assigning different backup priorities to different computing devices based on their specific data characteristics. Each device's backup priority is determined by analyzing local factors such as data type (electronic communications vs. image files), data size, and access frequency patterns. This allows the system to automatically differentiate between important and unimportant data without requiring manual classification, maintaining operational simplicity while preventing loss of critical information through priority-based backup scheduling.
3Measurement precision
If backup priority determination is performed manually, then accuracy of data importance assessment is improved, but time consumption and operational complexity increase
Solution Approach 1:
The patent implements self-service by enabling computing devices to automatically determine their own backup priorities based on their data characteristics and system conditions. The system continuously monitors data attributes (type, size, access frequency) and calculates backup priority scores without manual intervention. This automated approach maintains accurate data importance assessment by using objective metrics while eliminating the time loss and operational complexity associated with manual priority determination, allowing the system to adapt dynamically to changing data patterns.
Data Source
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AI summary
A remote agent for providing data protection services includes persistent storage and a backup manager. The persistent storage stores lifecycle policies. The backup manager updates an asset catalog using an asset map associated with a virtual machine; generates a lifecycle policy of the lifecycle policies using the asset catalog; and generates a backup for the virtual machine using the lifecycle policy. The lifecycle policy prioritizes generating the backup for the virtual machine over generating a second backup for a second virtual machine.