QoS Fluctuation Feedback for 5G Application Reliability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for deploying applications over 5G private mobile communication networks dynamically adapt QoS, but fail to prevent outages for non-elastic applications and do not optimize long-term quality of experience, lacking proactive resource management and a priori knowledge of application robustness to QoS fluctuations.
Innovation Solution
A method providing feedback information to applications about the expected probability of QoS fluctuations over time, allowing for proactive adaptation and resource allocation, using network functions like NWDAF and NEF, which predict and classify QoS parameters to offer statistical distributions and long-term satisfaction metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the 5GCN dynamically adapts QoS by reducing QoS when resources are insufficient, then resource utilization is improved, but application reliability deteriorates due to outages for non-elastic applications
Solution Approach 1:
The patent applies preliminary action by providing the application with advance information about expected QoS fluctuations and statistical distributions before they occur. The network function sends predictions about future QoS conditions, allowing the application to proactively adapt its behavior or switch to alternative QoS profiles before outages happen, thus maintaining reliability while enabling dynamic resource management.
Solution Approach 2:
The patent implements feedback by establishing a closed-loop communication where the network function continuously monitors QoS conditions, predicts future fluctuations, and provides this information back to the application. The application can then respond by adjusting its resource requirements or switching QoS profiles, creating a feedback mechanism that balances resource utilization with application reliability.
2Reliability
If the 5GCN provision too much resource when available, then application reliability is improved, but network efficiency deteriorates due to resource waste
Solution Approach 1:
The patent applies dynamics by enabling flexible, adaptive resource provisioning based on real-time and predicted QoS conditions. Instead of static over-provisioning, the system dynamically adjusts resource allocation by allowing applications to switch between different QoS profiles according to actual network conditions and predictions, optimizing both reliability and efficiency.
Solution Approach 2:
The patent uses parameter changes by allowing applications to modify their QoS profile parameters (such as data rate, latency requirements, packet loss tolerance) based on predicted network conditions. This enables the system to provision resources efficiently by matching actual application needs with available network capacity, avoiding both over-provisioning and under-provisioning.
3Productivity
If the 5GCN abusively rejects QoS requests, then network efficiency is improved, but application reliability deteriorates
Solution Approach 1:
The patent applies preliminary action by providing the application with advance knowledge of expected QoS conditions and statistical distributions before QoS requests are made. This allows the application to make informed decisions about resource requirements and switch to appropriate alternative profiles, preventing the network from having to reject requests due to unforeseen resource constraints.
Solution Approach 2:
The patent implements feedback by continuously communicating predicted QoS conditions and actual network status to the application, enabling the application to adjust its requests accordingly. This feedback loop prevents abusive rejections by aligning application requests with actual and predicted network capacity.
4Adaptability or versatility
If the application dynamically reacts to current QoS response, then adaptability is improved, but long-term QoE optimization deteriorates due to lack of proactive management
Solution Approach 1:
The patent applies preliminary action by providing the application with predicted QoS conditions and statistical distributions in advance, enabling proactive adaptation strategies. The application can plan resource allocation, switch QoS profiles, or adjust its behavior based on predictions before actual QoS degradation occurs, optimizing long-term QoE rather than just reacting to current conditions.
Solution Approach 2:
The patent implements feedback by establishing continuous communication between the network function and application, where the application receives both current QoS status and future predictions. This enables the application to combine reactive responses to current conditions with proactive adjustments based on predictions, achieving both adaptability and long-term QoE optimization.
Data Source
Figure 1~2
Figure 3~4
AI summary
The invention relates to a method for providing feedback to an application, the method being implemented by at least one network function of a mobile communication network, the method comprising: - obtaining an applicative request indicating a reference quality-of-service profile and at least one alternative quality-of-service profile; and - providing feedback information relating to a probability of fluctuation of quality-of-service output by the mobile communication network for the application function over a time period between a plurality of levels, a level corresponding to a quality-of-service profile indicated in the applicative request. The invention further relates to a corresponding device and to a corresponding computer program.