Network Container Provisioning via Automated Template Orchestration
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Solution Overview
Problem
The existing cloud computing services face challenges in quickly provisioning virtualized data centers due to the complexity of configuring hundreds of variables and parameters, which often requires days or weeks, leading to administrative and configuration errors, and inefficient resource utilization.
Innovation Solution
The implementation of resource management tools that automate the provisioning of network containers and virtual machine associations through a self-service portal, using pre-defined templates and orchestration engines to configure physical network infrastructure, reducing the need for manual intervention and enabling rapid scaling of virtualized data centers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration of virtualized data centers is performed by network engineers, then configuration accuracy can be maintained through expert knowledge, but service provisioning time increases to days or weeks
Solution Approach 1:
The system implements self-service automation where the virtualized data center configuration is performed automatically by the system itself rather than requiring manual intervention from network engineers. The orchestration engine executes pre-defined workflows and templates to provision services, allocate resources, and configure network settings autonomously, eliminating the time-consuming manual configuration process while maintaining reliability through validated automation scripts.
Solution Approach 2:
The system applies preliminary action by pre-defining configuration templates, workflows, and parameter sets for common virtualized data center scenarios. These templates are prepared in advance with all necessary configuration details, so when a provisioning request is made, the system can quickly instantiate pre-configured settings rather than creating configurations from scratch, significantly reducing provisioning time while ensuring consistency and accuracy.
2Adaptability or versatility
If hundreds of variables and parameters are manually specified for virtualized data center configuration, then comprehensive control over system settings is achieved, but operational complexity and administrative errors increase
Solution Approach 1:
The system segments the complex configuration process into distinct, manageable components using templates and workflows. Each template represents a specific aspect of virtualized data center configuration (e.g., network settings, compute resources, storage allocation), allowing the overall complex task to be broken down into smaller, standardized units that can be independently managed and validated, reducing operational complexity while maintaining comprehensive control.
Solution Approach 2:
The system implements universality by creating multi-functional templates that can handle multiple configuration variables and parameters within a single standardized framework. These templates are designed to work across different service types and configurations, providing a universal approach to managing diverse configuration requirements without requiring separate manual processes for each variable, thereby simplifying operations while preserving adaptability.
3Reliability
If physical computing infrastructure is maintained to meet peak demand, then service availability during high-demand periods is ensured, but resource utilization efficiency decreases during low-demand periods
Solution Approach 1:
The system applies dynamics by enabling flexible, on-demand scaling of virtualized data center resources. The automated provisioning system can dynamically allocate and de-allocate computing, storage, and network resources based on real-time demand conditions. During peak demand, additional resources can be rapidly provisioned; during low-demand periods, resources can be released back to the pool, maintaining service availability when needed while optimizing resource utilization efficiency throughout the cycle.
Data Source
AI summary
Provisioning of network containers provided as part of a cloud computing service. A network container defines a logical network topology to be hosted on a physical network infrastructure. One or more templates are populated using parameters and a network container profile associated with a requested network container. The requested network container is provisioned by translating the populated templates into a sequence of commands executed to configure the physical network infrastructure to host the requested network container.