Automated Disaster Recovery Plan Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current disaster recovery (DR) solution planning for IT systems is manual, error-prone, and time-consuming, often resulting in over-provisioning and lacking automation, especially in multi-site environments with diverse data types and changing workloads, where experts are scarce and best practices are not consolidated.
Innovation Solution
A computer-implemented method formulates an integrated DR plan by identifying entity types, data containers, and disaster types, using composition models and template libraries to select optimal replication technology solutions across multiple storage stack levels, considering various technologies and environments, and automatically generating and refining DR strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual DR solution planning is used, then designers can formulate DR plans based on user requirements, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical planning processes with an automated computer-implemented system that uses composition models to generate DR plans. The system automatically selects replication technologies and configurations based on input requirements, eliminating manual errors and reducing planning time while maintaining or improving plan accuracy through systematic algorithmic approaches.
Solution Approach 2:
The DR planning system performs self-service by automatically generating plans based on user input requirements without requiring expert intervention in the actual plan formulation. The composition model evaluates different replication technologies and configurations autonomously, selecting optimal solutions based on predefined criteria and constraints.
2Adaptability or versatility
If manual DR solution planning is used, then designers can customize DR plans, but the process is error-prone and lacks automation
Solution Approach 1:
The patent segments the DR planning process into distinct modular components: input requirement collection, composition model evaluation, replication technology selection, and plan generation. This segmentation allows each component to be optimized independently while maintaining overall customization capability, and enables automated execution of each segment without requiring manual coordination between different planning aspects.
3Ease of manufacture
If multiple replication technologies are considered, then cost-effective DR solutions can be found, but the solution space becomes large and complex
Solution Approach 1:
The composition model applies local quality by evaluating different replication technologies and configurations against specific local requirements for each data type and disaster scenario. Instead of uniformly applying all possible solutions, the system selectively evaluates only those technologies that meet the specific RTO, RPO, and cost constraints for each particular DR scenario, reducing the effective solution space while maintaining cost-effectiveness.
Solution Approach 2:
The system manages solution space complexity by dynamically changing evaluation parameters based on input requirements. The composition model adjusts which replication technologies are evaluated and which constraints are applied based on the specific disaster types, data types, and organizational requirements, effectively navigating the large solution space without requiring manual analysis of all possibilities.
4Reliability
If experts design DR solutions, then best practices can be applied, but expertise is scarce and best practices are not consolidated
Solution Approach 1:
The patent consolidates expert knowledge into an automated composition model that encodes best practices as systematic evaluation criteria and selection rules. The model automatically applies these consolidated best practices to generate DR plans, eliminating the need for individual expert intervention while maintaining or improving solution quality through consistent application of proven methodologies.
Data Source
AI summary
Formulating an integrated disaster recovery (DR) plan based upon a plurality of DR requirements for an application by receiving a first set of inputs identifying one or more entity types for which the plan is to be formulated, such as an enterprise, one or more sites of the enterprise, the application, or a particular data type for the application. At least one data container representing a subset of data for an application is identified. A second set of inputs is received identifying at least one disaster type for which the plan is to be formulated. A third set of inputs is received identifying a DR requirement for the application as a category of DR Quality of Service (QoS) class to be applied to the disaster type. A composition model is generated specifying one or more respective DR QoS parameters as a function of a corresponding set of one or more QoS parameters representative of a replication technology solution. The replication technology solution encompasses a plurality of storage stack levels. A solution template library is generated for mapping the application to each of a plurality of candidate replication technology solutions. The template library is used to select a DR plan in the form of a replication technology solution for the application.