Automated Deployment Rollback via Metric Monitoring
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Solution Overview
Problem
In cloud computing environments, customers face challenges in managing changes to resource deployments, as these changes can lead to undesired impacts on performance, accuracy, latency, or availability, making it difficult to determine and correct negative effects, often resulting in undesirable unavailability or other issues.
Innovation Solution
The implementation of an automated system that monitors specific metrics or behaviors post-change, allowing customers to specify acceptable and unacceptable values or ranges, and automatically rolling back changes if metrics fall outside acceptable ranges, thereby reverting the deployment to a previous state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If changes are made to resource deployment to improve functionality or performance, then new capabilities or efficiency are achieved, but negative impacts on accuracy, latency, or availability may occur
Solution Approach 1:
The system performs preliminary actions by creating a snapshot of the deployment state before changes are applied. This allows the system to quickly revert to the previous state if the change causes unacceptable performance degradation, thus maintaining reliability while enabling deployment flexibility.
Solution Approach 2:
The system implements feedback mechanisms by monitoring performance metrics after deployment changes and automatically triggering rollbacks when metrics fall outside acceptable ranges. This closed-loop control ensures that reliability is maintained while allowing adaptive changes.
2Measurement precision
If manual monitoring and correction of deployment changes is performed, then control over performance impact is achieved, but time is lost and unavailability increases
Solution Approach 1:
The system performs self-service by automatically detecting performance degradation and executing rollback operations without human intervention. This eliminates the time loss associated with manual monitoring and correction while maintaining precise control over performance metrics through automated threshold-based triggers.
Solution Approach 2:
Automated feedback loops continuously monitor performance metrics and immediately trigger corrective rollback actions when thresholds are breached, eliminating the delay inherent in manual processes while preserving measurement precision through systematic metric tracking.
3Ease of operation
If deployment changes are implemented without automated rollback, then operational simplicity is maintained, but negative impacts on accuracy, latency, or availability cannot be quickly corrected
Solution Approach 1:
The rollback mechanism operates autonomously without requiring complex manual procedures, maintaining ease of operation. When performance degradation is detected, the system automatically executes pre-configured rollback actions, preserving reliability without adding operational complexity to the deployment process.
Solution Approach 2:
Rollback configurations and snapshots are prepared in advance before deployment changes occur. This preliminary preparation enables instant reversal of problematic changes without requiring complex decision-making or manual intervention during incidents, thus maintaining operational simplicity while ensuring performance stability.
Data Source
AI summary
A customer having a deployment in a resource provider environment can request one or more changes to the deployment using one or more application programming interface (API) requests. Along with the one or more changes, the customer can specify one or more metrics or behaviors, or a function thereof, to be monitored for the deployment for at least a period of time after the change is implemented. The customer can also specify acceptable or unacceptable values or ranges for the metrics. If the value of a specified metric is determined during the monitoring to have an unacceptable value, the change can be automatically rolled back or undone. The roll back in some embodiments takes the form of a change in state to yet another state that will cause the deployment to operate similar to a state before the change was implemented.