Automated Software Deployment with Canary Rollback
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Solution Overview
Problem
Current software deployment methods are inefficient due to manual review processes, inconsistent human judgments, misconfigurations from updating hardware infrastructure, and the inability to rollback to previous versions if new updates fail, leading to slow and unreliable deployment of software.
Innovation Solution
Implementing automated canary deployment methods, continuous integration and deployment pipelines, and machine intelligence to monitor performance, automatically redirect user traffic, and manage infrastructure, enabling rapid and reliable deployment with minimal human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review process is used for code deployment, then human judgment can evaluate code quality, but deployment speed is slow and judgments are inconsistent
Solution Approach 1:
The patent replaces manual human review processes with automated machine intelligence systems that use algorithms and models to evaluate code quality, performance metrics, and deployment readiness. This substitution eliminates human judgment inconsistency while dramatically increasing deployment speed through automated decision-making systems.
Solution Approach 2:
The system enables self-service deployment where the automated platform independently evaluates code, determines deployment readiness, and executes deployment without requiring manual human intervention at each step. The machine intelligence system serves itself by making autonomous decisions based on predefined criteria and real-time metrics.
2Productivity
If software is deployed on existing hardware infrastructure, then resource utilization is efficient, but misconfigurations occur when hardware is updated and rollback is not possible
Solution Approach 1:
The patent segments the deployment process by creating isolated deployment environments where code can be tested and validated before production deployment. This segmentation allows independent evaluation of new code without affecting existing hardware configurations, enabling safe rollback if issues are detected while maintaining efficient resource utilization through controlled environment separation.
Solution Approach 2:
The system implements beforehand cushioning by performing comprehensive automated testing, performance monitoring, and validation in staging environments before production deployment. This preparatory cushioning detects potential misconfigurations and issues in advance, providing a safety buffer that enables rollback if production deployment fails while maintaining resource efficiency.
3Productivity
If new code replaces old version in production, then latest features are available, but rollback is impossible if errors are introduced
Solution Approach 1:
The patent implements discarding and recovering by maintaining previous working versions of code and enabling automated rollback to those versions if deployment errors are detected. The system can discard problematic new code and recover the prior stable version, ensuring continuous feature delivery while protecting against errors through automated version management and recovery mechanisms.
Solution Approach 2:
The system provides beforehand cushioning by implementing automated monitoring and validation systems that detect errors immediately after deployment. This cushioning mechanism prepares rollback procedures in advance and maintains version history, enabling quick recovery if errors are introduced while continuing to deliver new features through controlled deployment processes.
4Reliability
If performance monitoring is performed manually, then deployment validation can be done, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual performance monitoring and validation with automated machine intelligence systems that continuously track performance metrics, compare against baseline thresholds, and validate deployment success automatically. This substitution eliminates time-consuming manual processes while maintaining rigorous performance validation through algorithmic analysis of real-time system metrics.
Data Source
AI summary
Methods and systems may be used to deploy software more quickly from development to a production environment. The methods and systems may speed up the process of developing and deploying new code. Integrations may be provided to monitor aspects of the system to provide statistics and metrics for better understanding and to automatically optimize certain aspects of the software development cycle.