UPF Load Balancing via Dynamic Thresholds and Predictive Analytics

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Solution Overview

Problem

The increasing demand for reliable, fast, and continuous content transmission in 5G networks can lead to overloading certain network portions, necessitating effective load balancing mechanisms for user plane functions (UPFs) and network slices.

Innovation Solution

Implementing systems and methods for UPF and network slice load balancing by using current load thresholds, predicted throughput, special considerations for low latency traffic, and predicted CPU and memory utilization, while maintaining multiple load-thresholds for each UPF and slice based on capacity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If network capacity is increased to meet growing demand, then content transmission speed and reliability are improved, but network portions become vulnerable to overloading

Engineering Contradiction:

Improvecontent transmission speedVSAvoidnetwork overload prevention

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent implements dynamic load balancing by continuously monitoring UPF load metrics and adjusting traffic distribution in real-time. The SMF dynamically selects target UPFs based on current load conditions, transforming the static network architecture into a dynamic system that adapts to changing traffic patterns and prevents overload

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system establishes a feedback loop where the SMF monitors UPF load metrics, receives load information from UPFs, and uses this feedback to make intelligent routing decisions. This closed-loop control ensures that traffic is distributed based on actual network conditions, preventing overload while maintaining high transmission speeds

Inventive Principle:

Principle #23Feedback

2Reliability

If load balancing is implemented to prevent overload, then network reliability is improved, but system complexity increases

Engineering Contradiction:

Improveload distributionVSAvoidload balancing mechanism

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The Session Management Function (SMF) serves as an intermediary between UPFs and the core network, centralizing the load balancing logic. Rather than distributing complex balancing algorithms across multiple components, the patent uses the SMF as a mediator that makes centralized routing decisions, simplifying the overall system architecture while maintaining reliability

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If multiple UPFs are used to distribute traffic, then network capacity is improved, but difficulty in selecting appropriate UPF increases

Engineering Contradiction:

Improvenetwork capacityVSAvoidUPF selection

Core Design Contradiction:

ProductivityVSDifficulty of detecting and measuring

Solution Approach 1:

The patent replaces manual or static UPF selection mechanisms with an automated, analytics-driven system. The SMF uses load metrics, capacity information, and traffic characteristics to automatically select appropriate UPFs, substituting mechanical decision-making processes with intelligent algorithms that simplify the selection difficulty

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250081221A1User plane function (UPF) load balancing based on current UPF load and thresholds that depend on UPF capacity

Publication Date: 2025.03.06 BOOST SUBSCRIBERCO LLC

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.