Open RAN CU-UP Pod Scaling Without Load Balancer Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing OpenRAN architectures face challenges in dynamically scaling user plane throughput to accommodate peak and non-peak hours, leading to potential service disruptions and resource inefficiencies, especially in latency-sensitive applications like industrial robotics and eHealth.
Innovation Solution
Implementing CU-UP as a cloud-native network function (CNF) with intelligent dynamic scaling, allowing for pod addition/deletion without load balancers to minimize latency and resource impact, and incorporating signaling to update endpoints dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional scaling methods with load balancers are used, then service continuity is maintained, but packet latency increases and system complexity increases
Solution Approach 1:
The patent extracts and removes the load balancer component from the scaling architecture. By eliminating the load balancer, the system achieves direct pod scaling without the additional latency layer that load balancers introduce, while maintaining service continuity through the alternative scaling mechanism described in the patent.
Solution Approach 2:
The patent segments the scaling operation into controlled phases where pods are added or removed in a managed sequence. This segmentation allows the system to scale without requiring a load balancer to redistribute traffic, thereby reducing latency while maintaining service continuity through careful state management.
2Productivity
If dynamic scaling is implemented, then resource utilization efficiency improves, but service disruption risk increases
Solution Approach 1:
The patent implements preliminary actions by preparing and validating scaling operations before they are executed. The system performs pre-checks and preparations that ensure scaling can occur without service disruption, allowing dynamic resource adjustment while maintaining reliability through advance planning and validation.
3Stability of the object's composition
If manual scaling intervention is used, then system stability is maintained, but operational complexity and cost increase
Solution Approach 1:
The patent implements self-service scaling where the system automatically monitors its own state and performs scaling operations without manual intervention. The CU-UP autonomously determines when scaling is needed and executes the scaling process, eliminating the need for manual operations while maintaining system stability through built-in decision-making logic.
4Adaptability or versatility
If cloud-native functions with dynamic scaling are deployed, then adaptability to network needs improves, but system complexity increases
Solution Approach 1:
The patent implements dynamic pod scaling where the system can automatically add or remove CU-UP pods based on real-time network conditions and traffic demands. This dynamic capability provides high adaptability to changing network needs while the automated nature of the process prevents corresponding increases in operational complexity.
Data Source
AI summary
A system is disclosed for providing Open RAN CU-UP high availability, the system comprising: at least one active CU-CP; at least one active CU-UP in communication with the at least one active CU-CP; and at least one standby CU-UP in communication with the at least one active CU-CP; wherein when a message may be received from a CU-CP that detects a failure of the at least one active CU-UP, the at least one standby CU-UP may be configured to take over and become an active CU-UP, thereby providing failover redundancy for the at least one active CU-UP.