5G Slice QoS Control Using Sliding-Window SLA Feedback
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Solution Overview
Problem
Current 5G network slicing mechanisms lack effective closed-loop QoS monitoring and control, leading to resource wastage, complaints, and traffic loss due to inadequate QoS provision for critical applications.
Innovation Solution
A network device provides QoS monitoring and control by calculating SLA scores using a sliding window model, generating configuration data, and implementing feedback-based updates to manage network resources efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If 5G network slicing mechanisms are implemented without closed-loop QoS monitoring and control, then network capacity and application support are improved, but resource wastage and QoS reliability deteriorate
Solution Approach 1:
The patent implements closed-loop QoS monitoring and control by continuously monitoring QoS parameters (throughput, latency, packet loss) and providing feedback to adjust network slicing configurations. This feedback mechanism ensures that critical applications receive guaranteed QoS while preventing resource wastage on non-critical traffic, thereby resolving the contradiction between network capacity and QoS reliability.
Solution Approach 2:
The patent dynamically adjusts network slicing parameters and resource allocation based on real-time QoS monitoring and application requirements. This dynamic adaptation allows the system to optimize resource distribution continuously, maintaining high network capacity while ensuring reliable QoS for critical applications through real-time reconfiguration of slice parameters.
2Reliability
If QoS monitoring and control mechanisms are added to 5G network slicing, then QoS reliability and resource efficiency are improved, but system complexity increases
Solution Approach 1:
The patent implements a unified QoS monitoring and control framework that serves multiple functions simultaneously: monitoring QoS parameters, enforcing QoS policies, adjusting network slicing configurations, and providing feedback control. This multi-functional approach consolidates what could be separate complex systems into a single integrated mechanism, improving QoS reliability while limiting the increase in overall system complexity.
3Measurement precision
If continuous QoS monitoring is implemented for all application flows, then QoS visibility and control precision are improved, but computational resource consumption increases
Solution Approach 1:
The patent applies QoS monitoring and control with different levels of precision to different application flows based on their criticality. Critical applications receive continuous, high-precision QoS monitoring and strict enforcement, while non-critical applications receive reduced monitoring and best-effort service. This local differentiation improves QoS visibility for important traffic while reducing unnecessary computational resource consumption on less important flows.
Data Source
AI summary
A device may receive network data identifying a topology of network devices of a network, quality of service (QoS) rules for user equipment (UEs) and application flows associated with the network, QoS profiles for a radio access network (RAN) of the network, and service data flow (SDF) templates. The device may process the network data, with a model, to calculate a network device service level agreement (SLA) score, for each of the network devices on a sliding window, to generate network device SLA scores, and may aggregate the network device SLA scores to generate an end-to-end SLA score on the sliding window. The device may generate QoS configuration data based on the end-to-end SLA score, and may implement the QoS configuration data.