Parameter-Driven Dynamic Disaster Recovery System
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Solution Overview
Problem
Organizations face challenges in ensuring data recoverability in cloud computing environments due to regional disasters or public cloud failures, which can impact their ability to use services effectively.
Innovation Solution
The implementation of parameter-driven service disaster recovery systems, which utilize recovery time objective (RTO), recovery point objective (RPO), and cost to serve (CTS) parameters to optimize disaster recovery configurations and ensure data availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional backup methods are used, then data recoverability is ensured, but recovery time and cost efficiency deteriorate
Solution Approach 1:
The system dynamically adjusts disaster recovery configurations based on real-time parameter evaluation (RTO, RPO, CTS). Instead of static backup schedules, the system continuously monitors system state and automatically modifies recovery strategies, enabling adaptive response to different failure scenarios and optimizing both recovery time and resource utilization.
Solution Approach 2:
The system changes key parameters (RTO, RPO, CTS) to optimize disaster recovery performance. By adjusting these parameters based on system conditions and requirements, the system can balance between recovery speed and cost efficiency, selecting optimal configurations for different disaster scenarios without manual intervention.
2Reliability
If traditional backup methods are used, then data recoverability is ensured, but cost efficiency deteriorates
Solution Approach 1:
The system changes key parameters (RTO, RPO, CTS) to optimize disaster recovery performance. By adjusting these parameters based on system conditions and requirements, the system can balance between recovery speed and cost efficiency, selecting optimal configurations for different disaster scenarios without manual intervention.
Solution Approach 2:
The system performs self-optimization by automatically evaluating disaster recovery configurations against multiple parameters and selecting the most cost-effective options. The automated framework eliminates the need for manual cost-benefit analysis and continuously optimizes resource allocation, reducing operational costs while maintaining data recoverability.
3Ease of manufacture
If fixed disaster recovery configurations are used, then implementation simplicity is maintained, but adaptability to different requirements deteriorates
Solution Approach 1:
The system dynamically adjusts disaster recovery configurations based on real-time parameter evaluation (RTO, RPO, CTS). Instead of static backup schedules, the system continuously monitors system state and automatically modifies recovery strategies, enabling adaptive response to different failure scenarios and optimizing both recovery time and resource utilization.
Solution Approach 2:
The automated optimization framework serves multiple functions: it evaluates RTO, RPO, and CTS parameters; generates optimal configurations; and adapts to different disaster scenarios. This universal system handles various recovery requirements through a single integrated platform, eliminating the need for separate manual configuration processes for different situations.
Data Source
AI summary
Disclosed are some implementations of systems, apparatus, methods and computer program products for implementing a database backup system.