Deployment Orchestrator Impact Analysis for CI/CD Safety
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Solution Overview
Problem
In large software systems, implementing Continuous Integration and Continuous Deployment (CICD) processes is challenging due to resource constraints and inter-dependencies, leading to incomplete functional and integration testing, making it difficult to determine the safety of deploying new software versions without impacting customers.
Innovation Solution
A deployment method that analyzes both technical and non-technical impacts of updates to determine a deployment factor, generating tailored deployment plans for deploying new versions of components in a computing environment, considering factors like update types, customer impact, and network traffic load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If phased deployments with traffic shaping are used for large systems, then deployment safety can be improved, but deployment complexity increases
Solution Approach 1:
The patent segments the deployment process into multiple phases with different traffic shaping strategies. Different deployment strategies (canary, blue-green, progressive) are segmented based on system characteristics and risk profiles, allowing complex deployments to be broken down into manageable stages that can be independently controlled and monitored.
Solution Approach 2:
The patent implements dynamic traffic shaping that automatically adjusts deployment parameters based on real-time system performance and error metrics. The traffic routing ratios, deployment speeds, and rollback thresholds are dynamically modified during deployment based on observed system behavior, enabling adaptive safety mechanisms that respond to actual system conditions rather than following rigid predefined paths.
2Reliability
If comprehensive functional and integration testing is performed, then deployment reliability improves, but deployment time increases
Solution Approach 1:
The patent performs preliminary impact analysis and risk assessment before deployment to identify which components require comprehensive testing and which can use lighter validation. By pre-identifying high-risk areas and pre-configuring test strategies, the system avoids unnecessary comprehensive testing of low-risk components, reducing overall deployment time while maintaining reliability for critical paths.
Solution Approach 2:
The patent applies partial testing strategies where the extent of functional and integration testing is adjusted based on the calculated deployment impact factor. For low-impact deployments, reduced testing is performed to save time, while high-impact deployments trigger more comprehensive testing. This selective approach ensures adequate validation without uniformly applying time-consuming comprehensive testing to all deployments.
3Productivity
If automated deployment is implemented, then productivity improves, but deployment factor determination complexity increases
Solution Approach 1:
The patent implements self-service automated impact analysis that automatically collects system metadata, dependency information, and performance metrics to calculate deployment impact factors without manual intervention. The system autonomously determines deployment strategies, traffic shaping parameters, and rollback thresholds by analyzing system state and comparing against predefined policies, enabling productivity gains while managing complexity through automation rather than manual processes.
Solution Approach 2:
The patent incorporates feedback loops where deployment impact analysis results from previous deployments are fed back into the system to refine future impact calculations. The automated system learns from historical deployment data, error patterns, and performance metrics to improve its impact assessment accuracy over time, reducing the complexity burden by leveraging accumulated knowledge rather than requiring complex analysis for every deployment.
Data Source
AI summary
A deployment orchestrator system is disclosed that determines an impact of deploying a new version of a component of an application deployed in a computing environment. The impact of deploying the new version of the component may be determined by generating a deployment factor for deploying the new version of a component based on analyzing information that identifies both a technical and a non-technical impact of an update made to the component. In certain embodiments, the deployment orchestrator system includes capabilities for generating different deployment plans for deploying the new version of the component. Each deployment plan is tailored based on a specific deployment factor determined for the deployment. The new version of the component is then deployed based at least in part on the deployment plan to different production regions of a production environment of the deployment orchestrator system.