Centralized Unit Deployment Automation
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Solution Overview
Problem
Deploying and managing cellular network components, such as Centralized Units (CUs), in a seamless and efficient manner is challenging due to the complexity of scaling networks and the need for significant time and resources.
Innovation Solution
A computer-implemented method and system for automated deployment and management of CUs, involving the receipt of deployment requests, determination of deployment parameters, generation of deployment data, and automatic execution of workflows to deploy and manage CUs within computing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual deployment methods are used for CUs, then deployment control and monitoring are possible, but deployment time and resource consumption increase significantly
Solution Approach 1:
The system enables self-service deployment through automated workflows where the deployment system automatically determines parameters, generates deployment data, and executes deployment without manual intervention. The orchestrator autonomously manages the deployment process based on received requests, eliminating the need for manual deployment operations while maintaining full control and monitoring capabilities.
Solution Approach 2:
The system performs preliminary actions by pre-determining deployment parameters and generating deployment data before actual deployment execution. The orchestrator prepares workflow definitions and deployment configurations in advance, allowing the deployment process to proceed automatically without real-time manual intervention, thus reducing deployment time while maintaining control.
2Productivity
If automated deployment is implemented, then deployment time is reduced, but system complexity increases
Solution Approach 1:
The orchestrator serves as an intermediary component that manages the complexity of automated deployment. It receives deployment requests, determines parameters, generates deployment data, and coordinates workflow execution, thereby centralizing the complexity management in a single component rather than distributing it throughout the entire system. This intermediary approach enables automation while containing system complexity.
Solution Approach 2:
The deployment system is segmented into distinct functional components: the orchestrator for managing deployment workflows, the parameter determination module, the deployment data generation module, and the workflow execution engine. This segmentation allows each component to handle specific aspects of deployment independently, reducing overall system complexity while enabling automated deployment through coordinated operation of these modular components.
3Manufacturing precision
If manual parameter determination is used, then deployment accuracy is maintained, but resource consumption and time increase
Solution Approach 1:
The system replaces manual mechanical operations with automated computational processes. Instead of manual parameter determination and deployment data generation, the orchestrator automatically determines deployment parameters and generates deployment data through computational workflows. This substitution maintains deployment accuracy through systematic parameter determination while reducing operational complexity by eliminating manual intervention.
4Reliability
If traditional deployment approaches are used, then vendor-specific solutions are provided, but scalability and flexibility are limited
Solution Approach 1:
The orchestrator is designed as a universal deployment management system that can handle multiple vendor-specific deployment scenarios through a single unified interface. It receives deployment requests, determines appropriate parameters based on vendor requirements, generates vendor-specific deployment data, and executes workflows for different CU types and vendors. This multi-functionality enables the system to maintain reliability for vendor-specific solutions while providing scalability and flexibility for diverse deployment scenarios.
Data Source
AI summary
Systems and methods for CU deployment is provided. An example method includes receiving, by one or more processors, a request to deploy one or more CUs; determining, by the one or more processors, parameters used to deploy the CUS; obtaining, by the one or more processors, the parameters; generating, by the one or more processors, CU deployment data that includes the parameters; and causing, by the one or more processors, an automatic deployment of the CUs within one or more computing environments based, at least in part, on parameters.


