Dynamic Data Restoration Prioritization for Complex Networks
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Solution Overview
Problem
Current data recovery techniques struggle with complex networks and varied components, leading to inefficient and prolonged recovery times, especially in large volume data environments, without adequately addressing dynamic changes in hardware and software configurations.
Innovation Solution
A method and system that utilize dynamic optimization techniques to generate a recovery model based on extracted parameters, calculate predict recovery times, establish task priorities, and incorporate user-defined recommendations to optimize data restoration in complex environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data recovery techniques are used in complex networks with varied components, then data recovery can be performed, but recovery time is prolonged and efficiency is reduced
Solution Approach 1:
The system performs preliminary actions by collecting data from multiple resources before actual recovery is needed, extracting parameters, generating tables, and calculating predicted recovery times in advance. This preparation enables faster and more efficient data recovery when actually needed, as the foundation is already in place.
Solution Approach 2:
The system dynamically adjusts recovery strategies based on extracted parameters and calculated predicted times. The task priority is established and updated dynamically based on user-defined restoration recommendations, allowing the system to adapt to changing conditions and optimize recovery efficiency in real-time.
2Productivity
If data recovery is performed in complex networks with large volume data, then data can be restored, but the complexity of network and varied components makes recovery difficult
Solution Approach 1:
The system extracts key parameters from the complex data and network information, separating the essential recovery information from the complexity. By taking out and focusing on these critical parameters, the system can manage and restore data effectively despite the underlying network complexity.
Solution Approach 2:
The system segments the recovery process into distinct phases: data collection, parameter extraction, table generation, predicted time calculation, and priority establishment. This segmentation allows each phase to be managed independently, reducing the overall complexity of handling large volume data in complex networks.
3Loss of time
If recovery operations are performed without dynamic optimization, then simple recovery can be executed, but recovery time cannot be optimized and business objectives may not be met
Solution Approach 1:
The system incorporates feedback mechanisms where user-defined restoration recommendations are used to update task priority and generate restoration models. This feedback loop allows the system to continuously optimize recovery operations based on actual business needs and conditions, reducing customer downtime while managing complexity through structured optimization.
Data Source
AI summary
A method, computer system, and a computer program product are provided for backup and restoration of data. Data is obtained from a plurality of resources during a plurality of tasks. The resources are located in one or more computing networks. A plurality of parameters are extracted from obtained data. A table is generated and stored that includes the parameters extracted and an associated related task. A predict time is calculated based on the table for recovery of data when any of the resources become unavailable. A task priority is established based on calculated predict time and the table to optimize and improve the predict recovery time. A restoration model is generated based on updated restoration recommendation. The restoration model is to be used during resource unavailability or failure of one or more of the resources.