Dynamic Network Slice Capacity Thresholds for QoS Admission Control
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Solution Overview
Problem
Existing wireless networks face inefficiencies in managing network slice capacity thresholds, leading to suboptimal resource utilization and potential degradation of Quality of Service (QoS) due to inadequate dynamic adjustment of network slice access control.
Innovation Solution
Implementing a Network Slice Admission Control Function (NSACF) that dynamically determines and adjusts per-slice capacity thresholds based on real-time analytics and predictive demand, using artificial intelligence/machine learning techniques, to optimize network resource allocation and maintain QoS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed capacity thresholds are used for network slices, then network management is simple, but resource utilization is suboptimal and QoS degrades under varying load conditions
Solution Approach 1:
The patent implements dynamic capacity thresholds that automatically adjust based on real-time network conditions, load metrics, and predicted demand. The NSACF continuously monitors network slice performance and modifies capacity thresholds dynamically, transforming the static threshold model into an adaptive system that optimizes resource utilization while maintaining manageable complexity through automated control.
Solution Approach 2:
The system employs feedback mechanisms where the NSACF monitors network slice load metrics, QoS parameters, and capacity utilization, then uses this information to adjust capacity thresholds. The feedback loop continuously refines threshold settings based on actual network performance and predicted demand, enabling optimal resource allocation without requiring complex manual management.
2Speed
If manual adjustment of capacity thresholds is used, then system complexity is low, but response time to changing network conditions is slow leading to QoS degradation
Solution Approach 1:
The NSACF operates as a self-service system that automatically monitors network conditions, predicts demand using machine learning, and adjusts capacity thresholds without human intervention. The system serves itself by continuously optimizing network slice capacity based on real-time metrics and predictions, achieving rapid response to load changes while keeping the adjustment mechanism manageable through automation.
Solution Approach 2:
The system uses machine learning models to predict future network slice demand and proactively adjusts capacity thresholds before actual load changes occur. This preliminary action based on predictions enables the system to respond faster to upcoming demand shifts, preventing QoS degradation before it happens rather than reacting after the problem arises.
3Adaptability or versatility
If dynamic per-slice capacity thresholds are implemented, then resource allocation is optimized, but the complexity of the control function increases
Solution Approach 1:
The NSACF is designed as a universal control function that handles multiple network slices simultaneously with a single integrated mechanism. Rather than implementing separate control logic for each slice, the NSACF provides multi-functional capacity management that adapts to different slice requirements through a unified framework, reducing overall complexity while maintaining high adaptability across diverse network slice scenarios.
Solution Approach 2:
The system achieves adaptability through parameter changes rather than structural complexity. The NSACF adjusts capacity threshold parameters dynamically based on network conditions and slice-specific requirements, using configurable parameters to handle diverse adaptation scenarios. This approach allows high versatility through parameter tuning while keeping the underlying control function structure relatively simple and manageable.
Data Source
AI summary
A system described herein, which may be implemented by a Network Slice Access Control Function (“NSACF”) of a wireless network, may monitor analytics information with respect to a plurality of network slices of a wireless network. The analytics information may be received from a Network Data Analytics Function (“NWDAF”) of the wireless network. The system may determine, based on the monitored analytics information, a capacity threshold for at least a particular network slice. The system may receive a request for access to the particular network slice; determine, based on the capacity threshold for the particular network slice, whether to accept or deny the request; and output, in response to the request an indication of whether the request is accepted or denied. The indication may be provided to a network function of the wireless network or to an external device via a Network Exposure Function (“NEF”).