Cloud Service Rollout Orchestration for Regression Mitigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cloud-based service update deployment methods face challenges in managing regressions, leading to potential failures and significant delays, as updates are deployed sequentially, creating a single point of failure and affecting the entire userbase if a regression occurs.
Innovation Solution
An orchestration system with a rollout service that automatically determines deployment policies, monitors for regressions, and mitigates their impact by using a database of previous updates, risk assessment, and planned fault insertion to test responses, ensuring rapid recovery and minimizing user disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential deployment of updates is implemented, then deployment control and risk management are improved, but deployment time and system availability deteriorate due to single point of failure
Solution Approach 1:
The patent divides the deployment process into independent parallel tracks using ring-based segmentation. Multiple rings (e.g., ring 1, ring 2, ring 3) can deploy different update versions simultaneously to different subsets of the userbase. This segmentation eliminates the single point of failure in sequential deployment while maintaining controlled risk exposure through isolated failure domains.
Solution Approach 2:
The system performs preliminary actions by pre-configuring multiple deployment rings with different update versions before actual deployment. Deployment policies are predetermined for each ring, allowing parallel execution without real-time coordination overhead. This preliminary setup enables rapid failover to alternative rings if regressions occur.
2Productivity
If multiple update versions are deployed simultaneously to different rings, then deployment productivity is improved, but regression impact management deteriorates due to lack of coordination
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor service health metrics across all deployment rings. When regressions are detected in any ring, the system automatically feeds this information back to adjust deployment policies, halt problematic deployments, or trigger rollbacks. This feedback loop maintains reliability while allowing parallel productivity gains.
Solution Approach 2:
The deployment system dynamically adjusts ring configurations and update assignments based on real-time performance data and regression detection. Rings can be activated or deactivated dynamically, and update versions can be reassigned between rings based on observed stability, enabling flexible management of parallel deployments.
3Reliability
If comprehensive monitoring and mitigation systems are implemented, then service reliability is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service automation where the deployment system automatically detects regressions, determines appropriate mitigation actions, and executes rollbacks or ring adjustments without human intervention. This automation reduces the operational complexity burden while maintaining high service reliability through continuous self-monitoring and self-correction.
4Reliability
If ring-based staged deployment is used, then regression risk is reduced, but deployment time increases due to sequential ring progression
Solution Approach 1:
The patent segments the userbase into multiple independent rings that can progress through deployment stages simultaneously. Instead of sequentially moving one ring at a time, multiple rings can be in different deployment phases at the same time, dramatically reducing total deployment time while maintaining the risk-mitigation benefits of staged deployment through isolated failure domains.
Data Source
AI summary
An orchestration system implements a rollout service that deploys a series of updates to a cloud service while minimizing an impact of a regression caused in the cloud service by one of the updates. The system includes an orchestrator host computer hosting the rollout service; a network interface with a network on which the cloud service is provided; and a database of deployment policy information and records of previous updates to the cloud service. The rollout service automatically determines a deployment policy for an update using the database, implements a deployment of the update according to the deployment policy, monitors for evidence of a regression caused by the update, and identifies occurrence of the regression caused by the update to the cloud service to enable mitigation of an impact of the regression.