Slice-Driven Network Function Deployment via Resource Matching
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Solution Overview
Problem
Conventional methods for deploying and configuring network functions in cellular networks are inflexible, non-scalable, time-consuming, and prone to inaccuracies and inefficiencies due to their inability to optimize resource utilization across a wide geographic area.
Innovation Solution
A slice-driven request management system that generates resource management profiles based on network slice profiles, optimizing the deployment of network functions by matching service requirements with available resources, allowing for automated deployment and customizable configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional deployment methods are used, then network functions can be deployed, but the deployment process is inflexible and time-consuming
Solution Approach 1:
The patent segments network functions into discrete network function instances that can be independently deployed and configured. Each network function is defined as a separate unit with specific characteristics, allowing flexible combination and deployment without requiring reconfiguration of the entire network architecture.
Solution Approach 2:
The patent employs preliminary action by pre-defining network function characteristics, deployment templates, and configuration parameters before actual deployment occurs. This allows automated deployment processes to execute rapidly by simply instantiating pre-configured network functions rather than configuring each one manually during deployment.
2Productivity
If conventional deployment methods are used, then network functions can be deployed, but resource utilization is ineffective
Solution Approach 1:
The patent utilizes parameter changes by allowing dynamic adjustment of network function characteristics such as deployment location, resource allocation, and configuration parameters. The system can modify these parameters to optimize resource utilization based on real-time network conditions and demand, transforming static deployments into dynamic, adaptive configurations.
Solution Approach 2:
The patent implements universality through a unified network function framework that can handle multiple types of network functions using the same deployment and management mechanisms. This multi-functional approach allows a single system to deploy various network functions (routing, firewalling, load balancing) using standardized templates, reducing overall system complexity.
3Adaptability or versatility
If each network function is individually configured, then specific customer preferences can be met, but the process becomes non-scalable
Solution Approach 1:
The patent applies preliminary action by pre-configuring network functions with customizable characteristics and deployment templates that can be automatically instantiated. Instead of manually configuring each network function individually, the system uses pre-defined templates that can be rapidly deployed and customized through parameter modification, enabling both scalability and customization.
Solution Approach 2:
The patent enables customization at scale by allowing individual network function instances to be configured through parameter changes rather than manual configuration. Each instance can have customized characteristics while maintaining the same deployment framework, allowing the system to scale by simply instantiating new parameterized instances rather than configuring each one from scratch.
Data Source
AI summary
The present disclosure relates to systems, methods, and computer readable media for facilitating placement of network functions based on a network slice profile that is received and based on internal knowledge of a cloud computing system having network resources thereon. The systems described herein involve tagging the network resources with various characteristics, generating resource management profiles including instructions that may be used to supplement information from the slice profile(s), and matching an incoming slice profile with a resource management profile. The systems described herein facilitate rolling out a deployment of network functions on the network resources in accordance with information from the resource management profile in a way that optimizes resources and allows automated placement of network functions based on a received network slice.