User Plane Instance Selection in Mobile Networks
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Solution Overview
Problem
Mobile networks face challenges in selecting optimal user plane and control plane instances for communication sessions, which can impact network performance and user experience due to varying resource demands and increased complexity from diverse mobile devices and applications, leading to issues like latency and packet loss.
Innovation Solution
The implementation of user plane and control plane selectors that analyze key performance indicators and selection criteria to dynamically select the most suitable instances for each communication session, using a combination of round robin, rule-based, and machine learning approaches to optimize resource allocation and minimize latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional static selection methods are used for user plane and control plane instances, then device complexity is reduced, but network performance deteriorates due to inability to adapt to varying resource demands and network conditions
Solution Approach 1:
The patent implements dynamic selection of user plane and control plane instances based on real-time network conditions and device requirements. The selection system continuously adapts to varying resource demands by evaluating current network state, device capabilities, and service requirements to determine optimal instance assignments, thereby improving network performance while managing complexity through structured decision-making frameworks
Solution Approach 2:
The system incorporates feedback mechanisms that monitor network conditions, resource utilization, and service performance to continuously optimize instance selection. By collecting and analyzing operational data from the network environment, the selection system adjusts its decisions to maintain optimal performance under changing conditions, resolving the contradiction between static simplicity and dynamic performance
2Reliability
If dynamic selection based on real-time conditions is implemented, then network performance is improved, but processing time increases due to analysis of multiple selection criteria
Solution Approach 1:
The patent implements preliminary actions by pre-evaluating and caching instance selection criteria and network condition parameters before actual session establishment. By preparing selection data structures and pre-computing optimal instance assignments based on anticipated conditions, the system reduces processing time during critical session setup while maintaining the benefits of dynamic, condition-based selection
Solution Approach 2:
The system dynamically adjusts selection parameters and weighting factors based on network conditions and service requirements. By changing the parameters used in selection calculations—such as prioritizing different performance metrics or adjusting threshold values—the system optimizes the balance between selection accuracy and processing speed for different operational scenarios
3Reliability
If multiple selection criteria are analyzed for each communication session, then service quality is improved, but device complexity increases due to multiple selection parameters
Solution Approach 1:
The patent segments the selection criteria into distinct, modular components that can be independently evaluated and combined. By dividing the complex selection process into separate assessment stages—such as evaluating user plane instances independently from control plane instances, or assessing different performance dimensions separately—the system manages complexity through structured organization while maintaining comprehensive service quality assessment
Data Source
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AI summary
A method (400) includes receiving from a control plane (160), a request for a user plane instance (152) that is configured to perform packet processing for a user equipment (102). The method also includes identifying a plurality of user plane instance candidates (212) associated with a base station (104). The plurality of user plane instance candidates are configurable by the control plane. For each user plane instance candidate, the method includes determining one or more selection parameters (114) corresponding to a subset of key performance indicators (112) for the base station. The method further includes selecting one of the plurality of user plane instance candidates to fulfill the request for the user plane instance from the control plane based on the one or more selection parameters determined for each of the plurality of user plane instance candidates.