Cloud Resource Provisioning Wrappers for AV Service Deployment
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Solution Overview
Problem
The complexity of autonomous vehicle (AV) software deployment and infrastructure management is increasing, necessitating a unified and automated approach to provisioning and deployment of services across diverse repositories, with a need for standardized deployment environments that can scale and replicate infrastructure quickly.
Innovation Solution
A deployment model using Crossplane composition custom resource definitions (CRDs) and Kubernetes (K8s) ecosystem to automate provisioning and deployment of cloud resources, integrating with PaaS/CI/CD subsystems, and employing tools like Kubernetes, Crossplane, Buildkite, Vault, and Argo CD to standardize and automate the deployment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual provisioning methods are used for cloud resources, then flexibility and customization are maintained, but deployment complexity and manual labor increase significantly
Solution Approach 1:
The patent introduces a wrapper as an intermediary layer between the provisioning system and cloud resources. This wrapper encapsulates the complexity of resource provisioning, presenting a simplified interface to users while handling complex automation tasks internally. The wrapper acts as a mediator that translates high-level deployment intentions into detailed provisioning actions, thereby reducing manual labor without exposing system complexity.
Solution Approach 2:
The provisioning system implements self-service capabilities through automated workflows that can independently provision, configure, and manage cloud resources. The system uses default wrappers that automatically handle resource creation and configuration without requiring manual intervention, enabling the system to serve itself and reducing the need for human operators while managing complexity internally.
2Productivity
If standardized deployment environments are implemented, then scalability and homogeneity improve, but adaptability to diverse repository structures decreases
Solution Approach 1:
The wrapper is designed as a universal component that can handle multiple types of cloud resources and diverse repository structures through a unified interface. It implements multi-functionality by supporting various resource types (compute, storage, networking) and adapting to different repository formats while maintaining consistent deployment workflows, thereby achieving both standardization and adaptability.
Solution Approach 2:
The system achieves adaptability through parameterized configurations within the wrapper. By allowing configurable parameters that can be adjusted based on specific repository structures and resource types, the wrapper maintains standardized deployment processes while adapting to diverse environments. This enables rapid deployment through standardization while preserving flexibility through parameter customization.
3Manufacturing precision
If comprehensive resource specifications are required, then deployment accuracy improves, but configuration complexity and time increase
Solution Approach 1:
The wrapper implements preliminary action by pre-configuring default parameters, templates, and resource specifications before actual deployment. Common configurations are prepared in advance within the wrapper, allowing the system to achieve high provisioning accuracy while reducing configuration time during actual deployment. The preliminary setup enables rapid instantiation of resources with accurate specifications.
Solution Approach 2:
The system uses copying mechanisms where proven resource configurations and specifications are replicated across multiple deployments. Once a resource specification is validated and optimized, it can be copied and reused through the wrapper, ensuring consistent accuracy while eliminating the need to reconfigure parameters manually for each deployment, thereby reducing configuration time.
Data Source
AI summary
A system for provisioning a plurality of resources in connection with a service to be provided in an autonomous vehicle (AV) infrastructure environment is described and includes a cloud platform executing a platform as a service (PaaS) cluster; a service-specific file specifying the plurality of resources; a values file specifying configuration information for each of the plurality of resource specified in the service-specific file; and a service for deploying the plurality of resources comprising the service on the cloud services platform using the service-specific file and the values file.