RAN-Aware MEC Traffic Control Service for Congestion Management
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Solution Overview
Problem
Radio access networks (RANs) face congestion issues due to significant traffic from Internet of Thing (IoT) devices to Multi-Access Edge Computing (MEC) systems, leading to service disruptions for other end devices, and vice versa, which affects network resource utilization and connectivity.
Innovation Solution
A traffic control service that uses RAN User Plane Congestion Information (RUCI) and MEC User Plane Congestion Information (MUCI) to determine congestion levels and implement remedial measures such as traffic shaping policies to manage traffic flow between MEC and RAN, and between RAN and end devices, thereby mitigating congestion and optimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If MEC systems process traffic locally to reduce core network load, then core network traffic is reduced, but RAN congestion increases due to significant traffic between RAN and MEC
Solution Approach 1:
The patent introduces a traffic control service as an intermediary between the RAN and MEC systems. This service receives congestion information from both sides, makes intelligent decisions about traffic routing, and coordinates remedial actions. The traffic control service acts as a mediator that balances the competing demands of reducing core network traffic while preventing RAN congestion, using policies such as traffic shaping and load shifting.
2Reliability
If traffic control policies are implemented to manage RAN-MEC traffic, then RAN congestion is mitigated, but network complexity increases
Solution Approach 1:
The patent segments the traffic control function into distinct components: a traffic control service that makes decisions, a congestion information collection mechanism that gathers data from RAN and MEC, and a remedial action execution system. This segmentation allows each component to be optimized independently and simplifies the overall system by dividing the complex task of congestion management into manageable segments.
Solution Approach 2:
The patent implements a feedback loop where congestion information from both RAN and MEC is continuously collected, analyzed by the traffic control service, and used to adjust traffic routing decisions. This feedback mechanism enables dynamic adaptation to changing network conditions, allowing the system to learn from past congestion events and make more effective decisions in real-time without requiring overly complex static policies.
3Reliability
If congestion remedial measures are applied to IoT traffic, then service disruptions are reduced, but network resource utilization decreases
Solution Approach 1:
The patent applies different quality of service (QoS) treatments to different traffic types and locations. Rather than applying uniform congestion control to all traffic, the system identifies local congestion hotspots and applies targeted remedial measures only where needed. This allows critical IoT traffic to receive prioritized treatment in specific areas while maintaining normal resource utilization in non-congested regions, thereby balancing reliability and productivity.
Data Source
AI summary
A method, a device, and a non-transitory storage medium are described in which a traffic control service is provided. The traffic control service may include using user plane congestion information pertaining to a radio access network and a multi-access edge computing system to manage congestion or anticipated congestion.