UPF Load Balancing for Low-Latency 5G Traffic
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Solution Overview
Problem
5G networks face potential overloading in certain portions due to increased demand from smartphones and IoT devices, necessitating effective load balancing of user plane functions (UPFs) to maintain network performance and reliability.
Innovation Solution
Implementing systems and methods for UPF load balancing using load thresholds, network data analytics, artificial intelligence, and machine learning to distribute PDU sessions across multiple UPFs, considering geographic proximity, throughput, latency, and CPU/memory utilization, ensuring efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If load balancing is implemented to distribute traffic across multiple UPFs, then network overload is reduced and reliability is improved, but system complexity and decision-making difficulty increase
Solution Approach 1:
The system pre-calculates and stores load thresholds for different UPFs before traffic arrives. When a PDU session needs to be established, the SMF simply queries the pre-computed thresholds and makes routing decisions without performing complex real-time analysis, thus improving reliability while keeping the decision process simple.
Solution Approach 2:
The patent replaces complex real-time load analysis with a simplified threshold-based comparison mechanism. Instead of continuously monitoring and analyzing UPF load dynamics, the system uses predetermined thresholds that can be easily queried and compared, substituting a complex mechanical monitoring system with a simpler information query system.
2Productivity
If traditional load balancing is used without special considerations, then general network traffic is distributed efficiently, but low latency traffic performance deteriorates
Solution Approach 1:
The system applies different load threshold criteria to different types of traffic. For low latency traffic, it uses latency-specific thresholds that prioritize UPFs with lower latency characteristics, while for general traffic it uses throughput-based thresholds. This local differentiation ensures that each traffic type is routed according to its specific performance requirements.
Solution Approach 2:
The patent segments the load balancing decision into separate threshold types: general load thresholds for standard traffic and latency-specific thresholds for low latency traffic. This segmentation allows the system to handle different traffic types with appropriate criteria without compromising overall network throughput or low latency performance.
3Productivity
If multiple load thresholds are maintained for different UPFs and traffic types, then traffic distribution is optimized, but information management complexity increases
Solution Approach 1:
The system maintains a unified threshold information structure that serves multiple purposes. The same threshold data structure is used for both general load balancing and latency-specific routing decisions, allowing the SMF to query appropriate thresholds based on traffic type without requiring separate information storage systems for different traffic categories.
Data Source
AI summary
Embodiments are directed towards embodiments are directed toward systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.