RAN Edge Excess Capacity Prediction for Dynamic Scaling
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Solution Overview
Problem
Existing radio access network (RAN) deployments face inefficiencies in resource utilization and latency, with manual configurations being time-consuming and expensive, and previous generations tying software to vendor-specific hardware, limiting flexibility and scalability.
Innovation Solution
Implementing a cloud delivery model for RANs with decoupled hardware and software, utilizing RAN-enabled edge servers for dynamic capacity management, predictive analytics, and creating far-edge availability zones to optimize resource use and minimize latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual configuration is used for RAN deployments, then deployment control is maintained, but deployment time and cost increase significantly
Solution Approach 1:
The system enables self-service deployment through automated provisioning mechanisms. The cloud delivery model allows RAN components to be automatically configured and deployed without manual intervention, with the system automatically managing resource allocation, software installation, and network configuration based on predefined templates and policies.
Solution Approach 2:
Manual mechanical configuration processes are replaced with automated software-based provisioning systems. The patent implements automated deployment pipelines, software-defined networking, and programmatic resource allocation that substitute for traditional manual hardware configuration and wiring, significantly reducing deployment time and complexity.
2Adaptability or versatility
If hardware and software are tied together in previous generations, then system stability is maintained, but flexibility and scalability are limited
Solution Approach 1:
The patent applies segmentation by separating hardware and software components into independent, modular units. RAN functions are divided into virtualized network functions that can be independently deployed, scaled, and managed on standardized hardware platforms, enabling flexible reconfiguration without affecting underlying infrastructure.
Solution Approach 2:
The cloud delivery model implements universality through standardized hardware platforms that can run multiple virtualized RAN functions and support various network deployments. The same hardware infrastructure can serve multiple purposes including 5G core network functions, edge computing, and traditional RAN operations, greatly enhancing scalability and adaptability.
3Reliability
If RAN resources are over-provisioned to handle peak demand, then service reliability is ensured, but resource waste increases
Solution Approach 1:
The system implements dynamic resource allocation that automatically adjusts RAN capacity based on real-time traffic patterns and demand. Virtualized network functions can be scaled up during peak periods and scaled down during off-peak periods, ensuring service reliability during high demand while minimizing resource consumption during low demand periods.
Solution Approach 2:
The patent utilizes parameter changes in resource allocation, dynamically modifying computational and network resource parameters based on load conditions. The system monitors metrics such as data traffic volume, user activity levels, and network utilization, and automatically adjusts resource allocation parameters to match actual demand, preventing both over-provisioning and under-provisioning.
Data Source
AI summary
Disclosed are various embodiments for predicting excess capacity for radio access network (RAN) edge systems. In one embodiment, resource consumption data is generated by monitoring resource consumption of a plurality of implementations of a distributed unit (DU) and/or centralized unit (CU) used in RANs. A maximum level of resource consumption is predicted for a particular implementation of the DU/CU to be used in a particular RAN based at least in part on the resource consumption data. Excess resource capacity is predicted on a RAN-enabled edge server on which the particular implementation of the DU/CU is deployed for a cell site of the RAN based at least in part on the maximum level of resource consumption.