Automated Failback Blueprint Generation for Disaster Recovery
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Solution Overview
Problem
The process of failback recovery after a disaster is highly manual, error-prone, and time-consuming, often resulting in oversubscription or undersubscription of production capacity, leading to costly remediation during steady state or failover recovery, due to the need for manual evaluation and reconfiguration of infrastructure and technologies between primary and disaster recovery sites.
Innovation Solution
A system utilizing predictive analytics and machine learning to generate failback and disaster recovery retrofit blueprints, which analyze data from disaster scenarios to recommend application mappings, workload consolidation, and infrastructure upgrades, thereby automating the failback process and optimizing recovery point and time objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual evaluation and reconfiguration of infrastructure is performed during failback, then customization and adaptability to specific disaster scenarios is improved, but the process becomes highly time-consuming and error-prone
Solution Approach 1:
The system performs preliminary actions by pre-generating failback blueprints that evaluate infrastructure reconfiguration needs before actual failback execution. The blueprint generation process analyzes application dependencies, infrastructure requirements, and potential issues in advance, allowing the system to prepare recovery strategies beforehand rather than performing manual evaluation during the critical failback window.
Solution Approach 2:
The system enables self-service by automatically generating failback blueprints without requiring manual intervention. The automated blueprint generation process evaluates disaster scenarios, analyzes infrastructure requirements, and produces recovery plans independently, eliminating the time-consuming manual evaluation and reconfiguration process while maintaining adaptability to specific disaster conditions.
2Manufacturing precision
If manual reconfiguration of infrastructure is performed during failback, then precise control over recovery processes is improved, but the process becomes error-prone and costly
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring failback process execution against the generated blueprints. The system compares actual recovery actions with the planned blueprint steps, detecting deviations and potential errors in real-time. This automated feedback loop ensures precise control over the recovery process while reducing human error, as the system can identify and correct issues that manual processes might miss.
Solution Approach 2:
The system replaces manual mechanical processes with automated computational processes. Instead of human operators manually evaluating and reconfiguring infrastructure during failback, the system uses automated blueprint generation and execution that systematically analyzes requirements and applies configurations. This substitution eliminates human error-prone activities while maintaining precise control through algorithmic decision-making and automated execution.
3Productivity
If infrastructure capacity is oversubscribed during failback, then resource utilization is improved, but costly remediation is required during steady state
Solution Approach 1:
The system performs preliminary capacity planning by evaluating infrastructure requirements in the failback blueprint generation phase. It analyzes application dependencies, resource demands, and capacity constraints before failback execution, identifying potential oversubscription issues in advance. This allows the system to optimize resource allocation plans beforehand, ensuring efficient resource utilization during failback while preventing costly remediation during steady state by avoiding capacity oversubscription.
4Loss of time
If automated failback processes are implemented, then recovery time is reduced, but complexity of the system increases
Solution Approach 1:
The system introduces an intermediary layer in the form of automated failback blueprints that mediate between the complex underlying infrastructure and the simplified recovery process. The blueprints encapsulate complex infrastructure evaluation, dependency analysis, and reconfiguration logic in a standardized format that can be automatically executed. This intermediary approach reduces recovery time by automating complex processes while managing system complexity through structured blueprint generation and execution frameworks.
Data Source
AI summary
Provided is a method, computer program product, and system for performing automated failover and/or failback recovery analysis using predictive analytics. A processor may monitor a disaster recovery (DR) life cycle during a DR scenario. The processor may monitor failover process activities in a DR production environment over a predetermined time period. Based on data collected during monitoring of the DR life cycle and the failover process activities in the DR production environment over the predetermined time period, the processor may generate, using machine learning, a failback blueprint plan to move production to a new production environment.