Disaster Recovery Assessment GUI for Configuration Comparison
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Solution Overview
Problem
Businesses face challenges in selecting an efficient and cost-effective disaster recovery solution due to the complexity and time-consuming nature of assessing risk and cost associated with data loss, as well as the need for specific technical expertise and equipment.
Innovation Solution
A computer-implemented method and application that provides a graphical user interface for assessing and planning data disaster recovery, allowing users to generate and compare different configurations by dragging and dropping components, calculating and displaying metrics such as capital expenses, recovery time, and data reliability, to help select an appropriate and cost-effective solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive disaster recovery assessment and planning is performed manually, then accurate risk and cost evaluation is achieved, but the process becomes time-consuming and complex
Solution Approach 1:
The patent replaces manual mechanical assessment processes with an automated computer-implemented system that uses algorithms to calculate risk scores, cost metrics, and recovery configurations. The system automatically processes disaster recovery data, performs calculations, and generates reports without manual intervention, thereby maintaining measurement precision while dramatically reducing the time required for assessment and planning.
Solution Approach 2:
The system transforms complex disaster recovery assessment into standardized parameter-based calculations. By defining specific parameters for risk evaluation (e.g., data criticality, recovery time objectives, cost factors) and using these as input variables, the system can quickly compute results through parameter-driven algorithms, achieving both accuracy and efficiency simultaneously.
2Adaptability or versatility
If detailed disaster recovery configurations are created and compared, then optimal solution selection is improved, but the complexity of the process increases
Solution Approach 1:
The patent segments the disaster recovery configuration process into distinct, manageable components including data source identification, recovery method selection, configuration parameter setting, and metric calculation. Each segment can be independently configured and evaluated, allowing users to create detailed comparisons without being overwhelmed by overall process complexity. The system handles the integration of these segments automatically.
Solution Approach 2:
The system introduces an intermediary computational layer that manages the complexity of configuration comparisons. This intermediary automatically processes configuration inputs, calculates associated metrics (RTO, RPO, cost, risk scores), and presents results in a standardized format, thereby enabling detailed solution comparison without requiring users to manually manage the underlying complexity.
3Reliability
If multiple disaster recovery configurations are generated and evaluated, then solution effectiveness is improved, but the time and resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing baseline metrics for different recovery methods and configurations. When evaluating multiple configurations, it retrieves and compares these pre-computed values rather than performing full calculations each time, thereby maintaining reliable assessment of disaster recovery effectiveness while significantly improving evaluation efficiency and reducing resource consumption.
Data Source
AI summary
A method for assessing the risk and cost for data loss and disaster recovery (DR) plans includes providing an application having a graphical user interface (GUI) comprising first and second windows arranged adjacent to each other. The first window comprises a catalog of components used to generate data disaster recovery (DR) configurations and the second window displays the generated DR configurations. A first DR configuration is generated in the second window and components are added to the first DR configuration by dragging and dropping components from the catalog into appropriate locations of the second window. Metrics for the first DR configuration are calculated and reported in the second window. A second configuration is also similarly generated in the second window and the metrics results are graphically compared to each other.