IaC Dependency Graph Sequencing for Multi-Cloud Stack Deployment
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Solution Overview
Problem
Existing infrastructure-as-code (IaC) tools face challenges in managing complex, multi-component deployments across multiple cloud environments, requiring manual sequencing or explicit configuration of dependencies, leading to inefficiencies in execution and maintenance, particularly in managing cloud environments, and the need for an innovative solution to address these inefficiencies.
Innovation Solution
The introduction of a 'stacks' feature within IaC tools that generates a dependency graph to manage and orchestrate multi-component, multi-deployment configurations, utilizing a gRPC interface for remote procedure calls, and enabling intelligent sequencing and parallel changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually sequence or explicitly configure dependencies for multi-root module deployments, then deployment control and reliability are improved, but operation complexity and time consumption increase significantly
Solution Approach 1:
The system enables self-service by automatically generating the dependency graph and determining deployment sequences without requiring manual user configuration. The IaC tool autonomously analyzes resource dependencies, constructs the execution graph, and sequences deployments based on detected relationships, eliminating the need for users to manually specify dependency ordering while maintaining reliable deployment control
Solution Approach 2:
The patent replaces the manual mechanical process of configuring dependencies with an automated computational system. Instead of users manually sequencing modules, the system uses graph generation algorithms and automated dependency detection to determine deployment order, substituting human-operated mechanical configuration with automated software-based dependency analysis and sequencing
2Manufacturing precision
If users deploy each root module one-by-one in correct order, then deployment accuracy is improved, but productivity and execution speed decrease
Solution Approach 1:
The system introduces dynamics by enabling parallel deployment of independent modules while maintaining sequential execution of dependent modules. The dependency graph allows the system to dynamically adjust execution patterns, deploying modules that have no dependencies or whose dependencies are already satisfied in parallel, while automatically sequencing modules that require specific ordering, thus optimizing both accuracy and speed
Solution Approach 2:
The patent segments the deployment process into independent parallel tasks based on the dependency graph. Modules with no interdependencies are segmented into separate parallel execution streams, while dependent modules form sequential segments. This segmentation allows simultaneous execution of independent operations while preserving required execution order for dependent operations, improving overall productivity without sacrificing deployment accuracy
3Adaptability or versatility
If workspaces are expanded to manage more resources, then system capability is improved, but execution speed decreases and blast radius increases
Solution Approach 1:
The system segments large workspaces into smaller modular units represented as nodes in the dependency graph. Each module can be independently analyzed, planned, and executed. This segmentation reduces the complexity of individual workspace units, improving execution speed while maintaining the ability to manage large-scale systems through coordinated module composition. The graph structure allows efficient tracking of dependencies across segmented modules without requiring the entire system to be processed as one large unit
Solution Approach 2:
The patent introduces dynamic workspace management where the system can adaptively adjust which modules are active, being planned, or paused based on current execution state and dependencies. This dynamic approach allows the system to maintain high capability by having many available modules while controlling execution speed by only actively processing modules that are ready for deployment based on dependency satisfaction, thus managing system capability and execution speed dynamically rather than statically
4Speed
If workspace refactoring is performed to reduce size, then execution speed is improved, but maintenance complexity and time investment increase
Solution Approach 1:
The system performs self-service refactoring by automatically generating the dependency graph and identifying optimal module boundaries and groupings based on actual resource relationships. Rather than requiring manual refactoring analysis, the system autonomously determines how to segment workspaces into modules that optimize both execution speed and maintainability, using the generated graph to guide modularization decisions and automatically updating the workspace structure accordingly
Solution Approach 2:
The patent implements feedback mechanisms where the dependency graph generation process continuously monitors workspace structure and performance characteristics. The system uses this feedback to automatically adjust module boundaries, identify refactoring opportunities, and optimize the workspace organization. This feedback-driven approach ensures that refactoring decisions are based on actual execution patterns and dependency relationships, improving execution speed while minimizing unnecessary maintenance complexity through data-driven optimization
Data Source
AI summary
A system and method employed by an infrastructure-as-code (IaC) tool that is configured for defining IaC configurations comprising containers of interconnected components of a cloud computing platform. A system includes a graph generator configured to generate a graph of dependencies between the interconnected components of the cloud computing platform based at least in part on user-defined input-output relationships between the interconnected components. The IaC tool is configured to use the graph of dependencies to automatically determine a sequence of the provisioning or deployment of the interconnected components of the cloud computing platform. The system further includes a remote procedure call interface configured to enable communications between the IaC tool and the cloud computing platform, the remote procedure call interface further configured to enable the provisioning or deployment of the interconnected components of the cloud computing platform, in the determined sequence.