Automated Cloud Network Deployment System
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Solution Overview
Problem
Manual configuration and deployment of network resources for cloud-based services become bottlenecks as they scale, requiring high programming skills and institutional knowledge, leading to scalability issues and single points of failure.
Innovation Solution
An automated deployment system with user interfaces that facilitate the specification and deployment of network resources and software components, allowing for scalable and efficient management of resources across multiple geographically distinct data centers, using abstraction layers and frameworks like Chef to automate configuration management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual configuration and deployment of network resources is used, then services can be deployed with existing expertise, but scalability is limited and bottlenecks occur as the number of resources increases to hundreds or thousands of servers
Solution Approach 1:
The patent introduces an automated deployment system that acts as an intermediary between users and the complex network resources. This system includes deployment agents, configuration management tools, and automated provisioning software that handle the complexity of deploying hundreds or thousands of servers, while users interact through simplified interfaces. The intermediary absorbs the complexity rather than requiring users to directly manage it.
Solution Approach 2:
The automated deployment system enables self-service capabilities where the deployment process automatically configures, provisions, and manages network resources without requiring manual intervention for each resource. The system includes self-configuration mechanisms, automated resource allocation, and self-healing capabilities that allow the infrastructure to manage itself, eliminating the bottleneck of manual deployment.
2Ease of operation
If manual deployment techniques are used, then detailed control over resource configuration is possible, but the process requires high programming skill and institutional knowledge creating a single point of failure
Solution Approach 1:
The automated deployment system provides self-service capabilities where standardized deployment templates and configurations are automatically applied. The system includes pre-configured best practices, automated validation, and self-healing mechanisms that eliminate the need for specialized expertise while ensuring reliable deployment. The institutional knowledge is embedded in the automated system rather than residing in individual employees.
Solution Approach 2:
The patent employs template-based deployment where proven, validated configurations are copied and replicated across multiple resources. Instead of manually configuring each resource from scratch requiring expert knowledge, standardized templates are automatically instantiated and deployed. This ensures consistency, reliability, and eliminates the single point of failure associated with individual employee expertise.
3Adaptability or versatility
If manual configuration is used for small numbers of resources, then customization and control are maintained, but the process becomes time-consuming and inefficient when scaling to large numbers of servers
Solution Approach 1:
The automated deployment system segments the deployment process into modular, reusable components and templates. Each resource type has standardized configuration segments that can be independently configured and then rapidly replicated. This segmentation allows customization at the template level while enabling rapid deployment of hundreds or thousands of resources through automated instantiation of these segments.
Solution Approach 2:
The system performs preliminary configuration work by pre-defining deployment templates, configurations, and best practices before actual resource deployment. These preliminary actions include creating standardized resource templates, pre-configuring software stacks, and establishing deployment workflows in advance. When resources need to be deployed, the preliminary work is automatically applied, dramatically reducing deployment time while maintaining adaptability through template customization.
Data Source
AI summary
Techniques are described which simplify and/or automate many of the tasks associated with the configuration, deployment, and management of network resources to support cloud-based services.