AI Orchestration Engine for Distributed Deployment Rollback
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Solution Overview
Problem
Deploying software in highly distributed heterogeneous computing environments is complex due to varied network connections, different providers, and components, making it difficult to monitor and roll back applications across thousands of geographic locations, leading to potential service disruptions.
Innovation Solution
The use of AI/ML models trained with telemetry data to monitor and detect unexpected behavior during deployment, allowing for automated rollback operations and minimizing service disruptions by generating deployment plans that consider geographical, topological, and policy-driven strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software is deployed across thousands of distributed clusters in thousands geographic locations, then service coverage and scalability are improved, but monitoring traffic to identify unexpected behavior becomes difficult and deployment complexity increases
Solution Approach 1:
An orchestration engine is introduced as an intermediary system that centralizes the management of software deployment across distributed clusters. The orchestration engine receives deployment requests, generates deployment plans, and coordinates rollback operations, thereby reducing the complexity of managing software across thousands of geographic locations while maintaining broad service coverage.
Solution Approach 2:
The deployment process is segmented into distinct phases: deployment plan generation, traffic monitoring, anomaly detection, and rollback execution. This segmentation allows each component to be managed independently, reducing overall deployment complexity while enabling scalable deployment across numerous clusters and geographic locations.
2Ease of manufacture
If traditional deployment monitoring methods are used across distributed environments, then implementation simplicity is maintained, but detection precision of unexpected behavior deteriorates
Solution Approach 1:
The system implements continuous feedback loops where telemetry data from distributed clusters is collected, analyzed by machine learning models, and used to detect unexpected behavior. This feedback mechanism maintains implementation simplicity by automating the monitoring process while significantly improving detection precision through AI-driven anomaly identification.
Solution Approach 2:
Traditional mechanical monitoring methods are replaced with machine learning-based detection systems. The ML models analyze telemetry data patterns to identify unexpected behavior with high precision, while the automated nature of the system maintains implementation simplicity by eliminating manual monitoring requirements.
3Ease of operation
If rollback operations are performed manually in distributed environments, then operational control is maintained, but response time and productivity deteriorate
Solution Approach 1:
The rollback operation system performs self-service by automatically detecting unexpected behavior through telemetry analysis and executing rollback operations without manual intervention. The orchestration engine autonomously manages the entire rollback process, maintaining operational control through automated decision-making while dramatically improving response time and productivity.
Solution Approach 2:
Rollback capabilities are prepared in advance by maintaining version history and deployment state information. When unexpected behavior is detected, the pre-prepared rollback mechanisms can be executed immediately, reducing response time while the orchestration engine maintains operational control through automated execution.
Data Source
AI summary
One example method includes deploying an application in a distributed computing environment. Telemetry data is collected that corresponds with the deployment of an application. The telemetry data is received by a machine learning model that was trained with test telemetry data to determine whether the deploying is successful or failed. A successful inference results in continued deployment and a fail inference results in a rollback of the application.