Proactive Data Recovery via Disaster Prediction
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Solution Overview
Problem
Current disaster recovery solutions for data storage sites are cumbersome and time-consuming, particularly for enterprise-scale organizations, as IT administrators must manually trigger and manage data restore operations, which can be challenging during natural calamities or ransomware attacks, involving multiple servers and cloud services.
Innovation Solution
A proactive data recovery system that includes a disaster prediction module to monitor parameters indicative of potential disaster events, a data restore plan repository, and a disaster recovery preparation engine to dynamically determine and execute data restore actions, creating a restore package and initiating recovery operations automatically in response to predicted disasters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data restore operations are performed by IT administrators, then data can be recovered, but the process is time-consuming and cumbersome
Solution Approach 1:
The system performs preliminary actions by automatically identifying data requiring restoration and preparing restore operations before disaster events occur. The disaster prediction module monitors parameters and triggers backup operations in advance, so when a disaster actually occurs, the data is already prepared for rapid restoration, eliminating manual intervention time.
Solution Approach 2:
The system enables self-service by automating the entire data identification and restore trigger process without requiring IT administrator intervention. The disaster prediction module autonomously monitors system parameters, identifies affected data, and initiates restore operations automatically, allowing the system to serve itself rather than relying on human operators.
2Reliability
If manual identification and triggering of restore operations is performed, then data can be restored, but the process becomes challenging during natural calamities or ransomware attacks
Solution Approach 1:
The system performs self-service by automatically monitoring disaster-related parameters, identifying affected data, and triggering restore operations without human intervention. This is particularly valuable during natural calamities or ransomware attacks when administrators may be unable to manually manage restore operations due to the severity of the disaster.
Solution Approach 2:
The system implements feedback mechanisms where the disaster prediction module continuously monitors system parameters and automatically adjusts restore operations based on the monitored data. This closed-loop feedback ensures that restore operations are appropriately triggered and managed based on actual system conditions, eliminating the need for manual assessment and control.
3Reliability
If IT administrators manually manage restore operations for multiple servers and cloud services, then data recovery can be accomplished, but substantial time and resources are required
Solution Approach 1:
The system enables self-service automation that handles the entire disaster recovery process for enterprise-scale environments without administrator intervention. The disaster prediction module automatically identifies affected data across multiple servers and cloud services, and the system autonomously triggers and manages restore operations, dramatically improving productivity compared to manual processes.
Solution Approach 2:
The system implements multi-functionality by providing a unified disaster recovery solution that can automatically handle restore operations across diverse enterprise infrastructure including multiple servers, virtual machines, and cloud services. This universal approach consolidates what would otherwise require multiple separate manual processes into a single automated system.
4Measurement precision
If manual monitoring and management of restore operations is performed, then data recovery completeness and correctness can be verified, but it becomes a significant challenge
Solution Approach 1:
The system implements comprehensive feedback mechanisms where the disaster prediction module continuously monitors restore operation parameters and automatically verifies completion and correctness. This automated feedback loop ensures precise measurement of data recovery status without requiring complex manual verification processes, maintaining measurement precision while reducing operational complexity.
Data Source
AI summary
A proactive data recovery system is provided. The system includes a memory having computer-readable instructions stored therein and a processor configured to execute the computer-readable instructions to access a data storage platform and to monitor a plurality of parameters indicative of a requirement of data restore and/or recovery for the data storage platform. The requirement corresponds to a predicted occurrence of a disaster event. The processor is further configured trigger backup of data stored in the data storage platform based upon the plurality of parameters to create a restore package and to initiate the data restore and/or data recovery operation for the data storage platform using the restore package in response to the occurrence of the disaster event.