Smart High Availability System for eMBB-URLLC Traffic Management
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Solution Overview
Problem
Current radio access network systems face challenges in efficiently managing and prioritizing traffic for UEs with critical and non-critical traffic classes, leading to suboptimal resource allocation and high availability issues, especially in scenarios with eMBB-URLLC combinations, where protecting mission-critical UEs and maintaining seamless service is costly and complex.
Innovation Solution
The implementation of a method that classifies UEs and pods into critical and non-critical classes, dynamically reassigns them based on traffic types, and employs load balancing and proactive reclassification to optimize resource allocation, using near-real-time and non-real-time RICs to manage UE entity pods and DPS pods, ensuring high availability and reducing data replication volume.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all UEs are assigned to the same Pod Class with full protection, then high availability is improved, but resource utilization deteriorates and costs increase
Solution Approach 1:
The system segments UEs into different Pod Classes (critical and non-critical) based on their traffic requirements. Critical UEs receive protected Pod assignments with standby redundancy for high availability, while non-critical UEs are assigned to shared Pods without full protection, optimizing resource utilization for each group separately.
Solution Approach 2:
Different quality levels of protection are applied locally to different UE groups. Critical UEs receive high-availability protected Pod assignments with dedicated standby resources, while non-critical UEs receive standard shared Pod assignments, allowing each UE to receive appropriate protection level based on its specific requirements.
2Reliability
If critical UEs are protected with dedicated Pods, then service continuity is improved, but device complexity increases
Solution Approach 1:
The system segments Pod management into automated critical and non-critical categories, with dedicated standby Pods only for critical UEs. The automated classification and assignment mechanisms manage the complexity internally, presenting a simplified interface while maintaining service continuity for critical users.
Solution Approach 2:
The system implements self-service through automated Pod classification, assignment, and reassignment mechanisms. The network automatically identifies critical UEs, assigns them to protected Pods with standby redundancy, and manages failover without manual intervention, reducing operational complexity while ensuring service continuity.
3Adaptability or versatility
If Pod reassignment is performed dynamically based on traffic type, then adaptability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary classification of UEs into critical and non-critical Pod Classes during initial attachment or when traffic patterns indicate a change in requirements. By proactively assigning UEs to appropriate Pod Classes before traffic demands arise, the system avoids time-consuming reassignment during actual service delivery.
Solution Approach 2:
The system implements dynamic Pod Class assignment that adapts to changing traffic conditions. When a UE's traffic type changes (e.g., from non-critical to critical), the system dynamically reassigns the UE to the appropriate Pod Class, ensuring adaptability while managing processing time through efficient monitoring and trigger-based reassignment.
4Productivity
If load balancing is applied across all Pods, then resource efficiency is improved, but QoS differentiation deteriorates
Solution Approach 1:
The system segments load balancing operations by Pod Class. Load balancing is applied within each class (critical and non-critical) separately, ensuring that critical UEs are distributed across protected Pods with appropriate QoS guarantees, while non-critical UEs share standard Pods for maximum resource efficiency. This prevents mixing of traffic types that would compromise QoS differentiation.
Solution Approach 2:
Different load balancing strategies are applied locally to different Pod Classes. Critical Pods use load balancing that preserves QoS differentiation and protection status, while non-critical Pods use aggressive load balancing for maximum resource efficiency. Each Pod Class receives the appropriate quality level of load balancing management.
Data Source
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AI summary
Systems and methods for classifying and assigning User Equipment (UE) traffic to multiple classes of pods. Pods are classified into critical traffic and non-critical traffic pods. UE can then be assigned and reassigned to pods based on service requirements.