MEC Orchestrator UE Behavior Classification for Traffic Routing
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Solution Overview
Problem
Next-generation 5G wireless communication networks face challenges in optimizing User Equipment (UE) mobility, energy saving, and traffic loading balancing within Mobile Edge Computing (MEC) systems, necessitating an optimization mechanism for efficient resource management and traffic routing.
Innovation Solution
An MEC orchestrator acquires computation-related and mobility-related information to classify UE behavior, determining whether to trigger handovers or Virtual Machine migrations, and provides instruction tables with routing information for optimizing traffic flow among MEC entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional MEC systems are used without optimization mechanisms, then system simplicity is maintained, but UE mobility handling, energy efficiency, and traffic loading balancing deteriorate
Solution Approach 1:
The MEC orchestrator performs classification of UE behavior types in advance based on mobility patterns and computation requirements. This preliminary classification enables proactive decision-making for handover and VM migration, improving mobility handling reliability without reactive complexity
Solution Approach 2:
The MEC orchestrator acts as an intermediary between MEC entities and UEs, centralizing the classification and decision-making functions. This intermediary approach improves coordination and reliability while managing system complexity in a controlled manner
2Productivity
If VM migration and handover operations are frequently triggered to improve traffic loading balancing, then traffic distribution improves, but system overhead and energy consumption increase
Solution Approach 1:
The system applies different handling strategies to different UE behavior types locally. Static UEs receive different treatment than mobile UEs, and different computation requirements are handled differently. This targeted approach improves traffic balancing efficiency while minimizing unnecessary VM migrations and energy consumption
Solution Approach 2:
The MEC orchestrator changes system parameters (handover triggering, VM migration decisions) based on classified UE behavior types. By adjusting these parameters dynamically according to UE characteristics, the system achieves effective traffic loading balancing while optimizing energy consumption
3Measurement precision
If comprehensive MEC computation-related information and UE mobility-related information are collected to improve classification accuracy, then UE behavior classification accuracy improves, but information processing overhead increases
Solution Approach 1:
The information processing is segmented into distinct categories: MEC computation-related information and UE mobility-related information. The MEC orchestrator processes these segmented information types separately to determine UE behavior types, improving classification accuracy while managing processing complexity through structured organization
Data Source
AI summary
A method performed by an MEC orchestrator includes: acquiring MEC computation-related information and User Equipment (UE) mobility-related information from an MEC system including a plurality of MEC entities, performing a classification procedure, based on the MEC computation-related information and the UE mobility-related information, to determine a behavior type of a UE, wherein the behavior type indicates whether to trigger a handover (HO) in the MEC system and whether to trigger a Virtual Machine (VM) migration in the MEC system, and providing an instruction table in response to the behavior type, wherein the instruction table including routing information for routing MEC traffic among one or more of the plurality of MEC entities.