Cognitive Disaster Recovery Workflow Automation
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Solution Overview
Problem
Current disaster recovery workflows often fail due to misconfiguration or environmental changes, requiring manual analysis and corrective actions, which is time-consuming and affects recovery time objectives, with no automated method to determine failures until execution, leading to inefficient disaster recovery processes.
Innovation Solution
A computer-implemented method that builds a knowledgebase from historical disaster recovery workflow executions, analyzes failed actions, and generates recommended new workflows with embedded corrective actions using natural language processing and weightage assignment to increase success rates, automating the correlation of corrective actions and their application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual analysis and corrective actions are used for failed disaster recovery workflows, then flexibility and adaptability are maintained, but time consumption increases and recovery time objectives are affected
Solution Approach 1:
The system performs self-service by automatically analyzing failed workflow actions, identifying root causes, and generating corrective actions without requiring manual intervention. The cognitive engine autonomously processes failure data, correlates it with historical information, and produces remediation workflows, thereby eliminating time-consuming manual analysis while maintaining operational effectiveness
Solution Approach 2:
The system performs preliminary action by pre-analyzing failure patterns and preparing corrective actions in advance. When a workflow failure occurs, the cognitive engine has already built a knowledge base of potential issues and solutions from historical data, enabling rapid deployment of pre-prepared remediation steps rather than starting analysis from scratch
2Loss of time
If automated methods are implemented to detect failures, then recovery time is reduced, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where the cognitive engine continuously monitors workflow executions, learns from failures, and updates its knowledge base. This feedback loop enables automated detection and correction while managing complexity through iterative improvement rather than requiring complex upfront design
Solution Approach 2:
The cognitive engine acts as an intermediary layer between the disaster recovery workflow system and the failure detection/correction processes. This intermediary abstracts the complexity of automated analysis and correction, presenting a simplified interface while handling sophisticated pattern recognition and decision-making internally
3Reliability
If historical data is analyzed to generate recommended workflows, then success rate increases, but data processing requirements increase
Solution Approach 1:
The system performs preliminary action by pre-processing and storing historical workflow data in an optimized format during normal operations. When failures occur, the cognitive engine queries this pre-organized knowledge base rather than performing full data analysis, significantly reducing processing requirements while maintaining high success rates through access to historical patterns
Solution Approach 2:
The system applies partial action by analyzing only the specific portions of historical data relevant to the current failure mode. The cognitive engine identifies and focuses on correlated failure patterns and successful remediations from history, rather than processing entire workflow datasets, thereby reducing energy consumption while maintaining effective correction capabilities
Data Source
AI summary
Generating a new disaster recovery workflow is provided. In response to determining that a failed action was detected during execution of a disaster recovery workflow, reasons and fixes corresponding to the failed action are acquired from a data source. A set of correlated corrective actions that are potential fixes for the failed action is identified based on natural language processing of the reasons and fixes corresponding to the failed action. A weightage value is assigned to each correlated corrective action in the set of correlated corrective actions based on a plurality of factors to form a set of corrective actions with weightage values. A recommended new disaster recovery workflow is generated by embedding the set of corrective actions with weightage values within the disaster recovery workflow.