Cloud Site Automation for Versioned Hardware Interoperability
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Solution Overview
Problem
Existing cloud computing infrastructure deployment processes are prone to errors and require significant manual intervention, leading to inefficiencies and inconsistencies in hardware and software configuration.
Innovation Solution
A system and method for automating the installation and configuration of cloud computing hardware and software using a network software repository, installation system, and machine learning algorithms to ensure error-free deployment and interoperability among components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used for hardware and software configuration, then flexibility and adaptability are maintained, but error rates increase and deployment efficiency decreases
Solution Approach 1:
The system enables automated self-service deployment where the cloud computing environment automatically configures hardware and software components without manual intervention. The automated workflow engine executes pre-defined deployment scripts and configuration routines, allowing the system to deploy and configure itself while maintaining high accuracy and efficiency.
Solution Approach 2:
The system performs preliminary configuration and validation actions before actual deployment. Deployment templates and scripts are pre-configured with best practices and validation rules, ensuring that when deployment occurs, it executes accurately and efficiently without manual intervention during the critical deployment phase.
2Reliability
If manual configuration processes are used, then operator control is maintained, but consistency and reliability of deployment decrease
Solution Approach 1:
The automated deployment system is segmented into distinct modular components including a workflow engine, configuration management module, validation module, and logging module. Each component handles specific aspects of deployment, making the overall complex system manageable through clear separation of concerns while ensuring consistent and reliable execution.
Solution Approach 2:
The system manages complexity by parameterizing deployment configurations through templates where variables can be adjusted without changing the underlying deployment logic. This allows consistent deployment patterns to be reused across different environments by simply changing parameters rather than rewriting complex configuration procedures.
3Productivity
If automated deployment is implemented, then deployment speed and consistency improve, but initial system setup complexity increases
Solution Approach 1:
The automated deployment system is designed as a universal platform that can deploy multiple types of cloud computing environments (virtual machines, containers, storage systems) using a single unified interface and workflow engine. This multi-functionality reduces the need for separate automation tools for different deployment scenarios, managing infrastructure complexity while maintaining high deployment speed.
Solution Approach 2:
The system uses deployment templates that can be copied and reused across multiple deployments. Once a deployment configuration is validated and working, it can be replicated indefinitely with minimal additional setup effort, allowing rapid deployment scaling without proportionally increasing system complexity.
4Reliability
If version control is applied to algorithms, then software reliability and traceability improve, but management overhead increases
Solution Approach 1:
The version control system provides automated feedback mechanisms that track algorithm versions, deployment status, and performance metrics. When an algorithm is updated, the system automatically notifies relevant stakeholders, validates the new version, and maintains an audit trail, reducing manual version management overhead while improving traceability and reliability.
Data Source
AI summary
A system for configuring a network, comprising a network software repository operating on a first processor and configured to store one or more algorithms for each of a plurality of hardware items and to implement version control for each of the one or more algorithms. A network software installation system operating on a second processor is configured to install a current version of one or more algorithms on each of a plurality of site-specific hardware items and to configure each of the plurality of site-specific hardware items to interoperate with each other.