Predictive 5G UPF Load Balancing Using UE Throughput
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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 network data analytics, artificial intelligence, and machine learning to predict throughput, CPU utilization, and memory utilization, and employing weighted scheduling algorithms to distribute PDU sessions across multiple UPFs based on load thresholds and geographic proximity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network capacity is increased to handle more smartphones and IoT devices, then bandwidth and speed are improved, but network portions may become overloaded in certain circumstances
Solution Approach 1:
The system performs preliminary load assessment by analyzing historical traffic patterns, current UPF load metrics, and predicted UE throughput before assigning PDU sessions to UPFs. This advance planning prevents overload conditions by distributing traffic proactively rather than reactively, ensuring network reliability while maintaining high throughput capacity
Solution Approach 2:
The load balancing system continuously monitors UPF load metrics, session distribution, and network traffic patterns, using this feedback to dynamically adjust session assignment decisions. This closed-loop control ensures that increased network capacity is utilized effectively while preventing any single UPF from becoming overloaded, thus maintaining both productivity and reliability
2Productivity
If load balancing is implemented without predictive analytics, then system complexity is reduced, but load distribution optimization is insufficient
Solution Approach 1:
The system introduces a Session Management Function (SMF) as an intermediary that sits between the UPFs and UEs, centralizing the predictive analytics and load balancing logic. This mediator handles the complexity of predicting UE throughput, analyzing historical patterns, and making intelligent session assignment decisions, while presenting a simplified interface to both UPFs and UEs, thus achieving high load distribution efficiency without overwhelming system complexity
Solution Approach 2:
The system utilizes multiple parameters including historical traffic patterns, current UPF load metrics, predicted UE throughput, and session type characteristics to make load balancing decisions. By changing from simple static load balancing to dynamic parameter-driven optimization, the system achieves superior load distribution efficiency while managing complexity through structured parameter analysis
3Reliability
If PDU sessions are distributed evenly across UPFs, then implementation simplicity is maintained, but geographic proximity and performance optimization are compromised
Solution Approach 1:
The system applies different assignment strategies to different UEs based on their specific characteristics, geographic location, and service requirements. Instead of uniform distribution, each PDU session is assigned to the UPF that is optimally suited for that particular UE's needs, whether that be geographic proximity, current load conditions, or predicted throughput capabilities, thereby enhancing connectivity reliability while managing complexity through localized decision-making rules
Data Source
AI summary
Embodiments are directed towards 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.