Cloud App Building System with Automated Service Templates
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Solution Overview
Problem
The development and deployment of applications involving interactions between various services and high-level functionalities, such as machine learning and neural networks, on cloud platforms are complex due to the need for understanding multiple components and infrastructures, leading to slowed development processes and integration issues.
Innovation Solution
A cloud-based application building system that initializes new services using templates with common libraries, security scans, monitoring, and automated resource provisioning, employing machine learning models to predict load changes and adjust computing resources, while allowing continuous deployment and self-service navigation of complex systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If developers manually integrate and deploy multiple cloud components and services, then customization and control are improved, but development time and complexity increase significantly
Solution Approach 1:
The system pre-configures templates that include common libraries, security scan pipelines, monitoring pipelines, and code coverage management before developers need to deploy services. This preliminary preparation eliminates the need for developers to manually integrate these components, significantly reducing development time while maintaining customization capabilities through template parameterization.
Solution Approach 2:
The system introduces an intermediary layer of automated pipelines and templates that mediate between developer code and cloud infrastructure deployment. These intermediaries handle the complex integration tasks automatically, allowing developers to focus on business logic while the system manages infrastructure provisioning, security scanning, and monitoring configuration.
2Ease of operation
If developers manually manage cloud infrastructure components, then control and customization are improved, but the number of required skills and understanding increases
Solution Approach 1:
The system enables self-service automation where pipelines automatically provision cloud resources, configure security settings, set up monitoring, and manage deployments without requiring manual intervention. The automated pipelines handle infrastructure management tasks independently, reducing the skill requirements for developers while maintaining full control through configuration files and template parameters.
3Reliability
If comprehensive security scanning and monitoring pipelines are implemented, then system reliability and compliance are improved, but provisioning time increases
Solution Approach 1:
Security scanning pipelines and monitoring configurations are pre-built and integrated into templates before service deployment. This preliminary configuration ensures that security and compliance checks are performed automatically and efficiently during provisioning, rather than requiring time-consuming manual configuration. The pre-defined pipelines execute comprehensive security scans and set up monitoring in parallel operations.
Data Source
AI summary
A system implements a cloud-based digital platform allows developers to build new applications/services and then deploy to cloud platforms among continuous deployment, A/B test, blue/green deployment, and canary deployment. The system configures a service mesh on top of a cluster of computers. The system initializes a new service via templates that include common libraries, security scan pipeline, monitoring as code pipeline, and code coverage management for internal policy compliances, as well automated cloud resources request and provisioning. One or more proxy services, that extract data from the data sources using filters, can be executed. The system may use machine learning based models that are trained using the data extracted by the proxy service. The system allows automatic provisioning, computation orchestration, storage requests, and artificial intelligence insight feedback, as well as automated self-services to navigate complex systems and reduce on-boarding times of the platform.