Inflight Deployment Acceleration via Production Pattern Extraction
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Solution Overview
Problem
Large transformation programs in computing environments face inefficiencies in accelerating inflight deployments due to lack of automation, standardization, and codification, leading to prolonged times in reviewing and deploying new applications, despite lessons learned from initial deployments not being effectively leveraged.
Innovation Solution
The method involves extracting and analyzing components from production deployments to build relationships and develop an enterprise-wide methodology, which is used to accelerate inflight deployments by comparing them against established gold standards, leveraging approved patterns and reference implementations, and utilizing traced metadata for continuous compliance at the application level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If production deployments are manually reviewed and analyzed for each inflight deployment, then deployment accuracy and compliance are improved, but deployment time and productivity deteriorate
Solution Approach 1:
The system performs preliminary extraction and analysis of components from production deployments to create reusable templates and patterns before they are needed for inflight deployments. This advance preparation enables automated comparison and compliance checking during actual deployment, resolving the contradiction between thorough review and fast deployment
Solution Approach 2:
The system creates copies of proven production deployment components and patterns, storing them as reusable templates. These copies can be automatically applied to inflight deployments, ensuring compliance with production standards while eliminating manual review processes, thus improving both reliability and productivity
2Manufacturing precision
If comprehensive component analysis and relationship building are performed for each deployment, then deployment quality and standardization are improved, but processing time and complexity increase
Solution Approach 1:
The system performs comprehensive component analysis and relationship building in advance during production deployments, creating standardized templates that capture all necessary relationships and dependencies. This preliminary standardization work eliminates the need for repeated analysis during inflight deployments, maintaining high precision while reducing time loss
Solution Approach 2:
The system transforms deployment components into standardized parameters and metadata that can be automatically compared and validated. By changing the representation of deployment data into structured, comparable formats, the system achieves comprehensive analysis efficiency without sacrificing standardization quality
3Stability of the object's composition
If enterprise-wide methodology is developed from each production deployment, then deployment consistency across the organization is improved, but system complexity and resource requirements increase
Solution Approach 1:
The system develops universal templates and patterns from production deployments that can be applied across multiple inflight deployments and different applications. These multi-functional templates ensure consistent deployment practices organization-wide without requiring separate complex systems for each deployment, thus improving consistency while managing complexity
Data Source
AI summary
Leveraging production deployments to accelerate inflight deployments in a computing environment. A component is extracted, which is identified in the production deployment. The identified, extracted component is analyzed to derive a set of data describing the identified, extracted component. A relationship between the data is built. The relationship is used to develop an enterprise-wide methodology that is utilized to accelerate development of an additional, inflight deployment by comparing the additional, inflight deployment against the developed methodology.