Declarative Infrastructure Provisioner DAG Dependency Management
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Solution Overview
Problem
Current cloud infrastructure services face challenges in efficiently managing dependencies and automating the provisioning and deployment of infrastructure resources across multiple regions, leading to manual effort and potential errors, especially as the number of service teams and regions grows.
Innovation Solution
A declarative infrastructure provisioner (DIP) generates and traverses directed acyclic graphs (DAGs) to manage dependencies between resources, publishing indications of resource availability and regenerating the DAG upon capability availability, enabling automated booting and deployment processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual provisioning and deployment processes are used across multiple regions, then flexibility and control are maintained, but manual effort and potential errors increase significantly
Solution Approach 1:
The system implements self-service automation where the provisioning system automatically discovers dependencies between resources, generates deployment plans, and executes provisioning operations across multiple regions without manual intervention. The system autonomously manages the entire provisioning lifecycle from dependency analysis to resource deployment.
Solution Approach 2:
The system performs preliminary dependency analysis and generates deployment plans before actual provisioning execution. By pre-computing the dependency graph and determining the optimal provisioning sequence in advance, the system ensures accurate and efficient resource deployment while avoiding manual errors.
2Adaptability or versatility
If the number of service teams and regions grows, then service coverage and capability increase, but system complexity and management difficulty increase
Solution Approach 1:
The system segments the complex multi-region provisioning problem into manageable components by representing dependencies as a directed acyclic graph (DAG). Each resource and its dependencies are broken down into discrete nodes and edges, allowing the system to handle complexity through structured decomposition rather than monolithic management.
Solution Approach 2:
The system changes the state representation from flat resource lists to hierarchical dependency graphs, enabling efficient tracking and management of resource relationships across multiple regions. This parameter transformation allows the system to scale to complex multi-region scenarios while maintaining manageable complexity through graph-based organization.
3Productivity
If automated provisioning is implemented, then efficiency and scalability improve, but handling of resource dependencies becomes more complex
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor resource provisioning status and update the dependency graph accordingly. When resources are provisioned or their state changes, the system automatically feeds this information back into the dependency model, enabling dynamic adjustment of the provisioning plan without manual intervention.
Solution Approach 2:
The system uses dynamic dependency graphs that adapt and evolve as resources are provisioned. Rather than static dependency definitions, the system maintains living models of resource relationships that automatically update based on current system state, enabling flexible and efficient handling of complex dependency scenarios.
Data Source
AI summary
Techniques are disclosed for managing dependencies in an orchestration service. A computer-implemented method can include operations performed by a declarative infrastructure provisioner (DIP). In some embodiments, the DIP parses configuration data associated with a computing system and generates a directed acyclic graph (DAG) for booting a first resource. The DAG may specify a dependency of the first resource on a capability of a second resource. The DIP may traverse the DAG and determine, based at least in part on the traversal, that the dependency has been reached. The DIP may publish, to a scheduling process, an indication that the first resource is awaiting availability of the capability of the second resource. In some embodiments, the DIP receives a subsequent indication that the capability is available, regenerates the DAG, and recommences traversal of the DAG. Additional operations for booting the first resource may be performed in accordance with the recommenced traversal.