5G UPF Load Balancing with Predictive UE Traffic Analytics
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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 current load thresholds, predicted throughput, low latency considerations, and CPU/memory utilization, combined with geographic and network slice load balancing, utilizing SMF to dynamically distribute PDU sessions across multiple UPFs based on network data analytics and AI/ML algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If 5G networks increase bandwidth and speed to meet growing demand from smartphones and IoT devices, then network capacity and transmission performance are improved, but certain portions of the network may become overloaded
Solution Approach 1:
The patent implements dynamic load balancing that continuously monitors UPF load conditions and adjusts session distribution in real-time. The SMF dynamically selects target UPFs based on current load thresholds, predicted throughput, and network conditions, allowing the system to adapt to changing traffic patterns and prevent overload while maximizing network capacity utilization.
Solution Approach 2:
The system performs preliminary load assessment and prediction before establishing PDU sessions. By using network data analytics to predict throughput and load conditions in advance, the SMF can pre-select appropriate target UPFs, preventing overload conditions before they occur and ensuring reliable network operation from the outset.
2Reliability
If load balancing distributes PDU sessions across multiple UPFs based on current load, then network reliability is improved, but system complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the SMF continuously monitors UPF load conditions, receives load threshold information from UPFs, and adjusts session distribution accordingly. This closed-loop control ensures reliable load balancing while automating the complexity management, as the system self-regulates based on real-time feedback rather than requiring complex manual configuration.
Solution Approach 2:
The SMF acts as an intermediary between UEs and multiple UPFs, centralizing the load balancing logic and session management functions. This intermediary approach simplifies the overall system architecture by consolidating control functions in the SMF, which handles the complexity of selecting appropriate target UPFs based on load conditions, while UPFs remain relatively simple forwarding entities.
3Measurement precision
If the system uses predicted throughput and network data analytics for load balancing, then load distribution accuracy is improved, but computational requirements increase
Solution Approach 1:
The system applies predictive analytics selectively based on network conditions and session types. Rather than performing full predictive analysis for every single PDU session, the SMF uses predicted throughput and load analytics when they would provide significant benefit, while relying on simpler load threshold-based decisions for routine sessions. This partial application of complex analytics reduces computational energy requirements while maintaining accuracy where it matters most.
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.