Intelligent Routing Service for Mobile Edge Computing Network Optimization
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Solution Overview
Problem
Multi-access Edge Computing (MEC) networks face challenges in efficiently managing and utilizing network resources to meet varying performance metrics and application demands, particularly due to limited resources and the need for scalable and flexible service provisioning with minimal latency.
Innovation Solution
A mobile edge network-based intelligent routing service that dynamically selects and manages network devices, including anchor nodes and server nodes across multiple MEC layers, based on performance metrics, subscription information, and device mobility, to optimize resource utilization and maintain performance criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network resources are expanded to meet varying performance metrics and application demands, then service quality and performance are improved, but network complexity and cost increase
Solution Approach 1:
The network is segmented into multiple MEC layers (first MEC layer, second MEC layer, third MEC layer) with different resource capacities and service capabilities. Each layer handles specific types of traffic and services, allowing the system to meet diverse performance requirements without requiring a single complex centralized network. The segmentation enables granular resource allocation and simplifies management by distributing functions across layers.
Solution Approach 2:
The intelligent routing service dynamically selects which MEC layer to route traffic to based on real-time performance metrics, application demands, and network conditions. This dynamic adaptation allows the network to optimize service quality for different applications without requiring static over-provisioning, thereby avoiding unnecessary complexity while maintaining high reliability.
2Speed
If MEC server resources are increased to reduce latency and improve performance, then service performance is improved, but resource cost and utilization efficiency worsen
Solution Approach 1:
Different MEC layers are assigned different resource qualities and capabilities. The first MEC layer (closest to end devices) handles latency-sensitive applications with minimal latency, the second layer handles moderate latency requirements, and the third layer handles bulk processing. This local quality differentiation allows each resource to be optimized for its specific function, improving overall efficiency without requiring all resources to be over-provisioned for the best-case scenario.
Solution Approach 2:
The intelligent routing service automatically monitors performance metrics and makes real-time routing decisions to place traffic at the appropriate MEC layer. This self-service mechanism ensures that resources are utilized efficiently based on actual demand, preventing waste while maintaining the capability to handle latency-sensitive traffic when needed.
3Stability of the object's composition
If network devices are statically assigned to fixed locations, then network stability is improved, but adaptability to device mobility and changing demands deteriorates
Solution Approach 1:
The system maintains stable network architecture through fixed MEC layers while implementing dynamic routing that adapts to device mobility and changing demands. The intelligent routing service continuously monitors device location, mobility patterns, and network conditions, dynamically selecting the optimal MEC layer for each traffic flow. This dynamic adaptation maintains network stability at the architectural level while providing versatility in handling mobile devices and varying service requirements.
Solution Approach 2:
The intelligent routing service implements feedback mechanisms by monitoring performance metrics and network conditions in real-time, then using this information to make informed routing decisions. This feedback loop allows the system to adapt to device mobility and changing demands while maintaining overall network stability through systematic control rather than ad-hoc changes.
4Productivity
If intelligent routing across multiple MEC layers is implemented, then resource utilization and performance are improved, but routing complexity and decision-making overhead increase
Solution Approach 1:
The routing decision-making process is segmented by MEC layers, with each layer having defined routing criteria and decision-making rules. The intelligent routing service evaluates traffic against layer-specific criteria and routes to the appropriate layer, avoiding the need for a single complex global optimization algorithm. This segmentation simplifies the routing logic while still achieving high resource utilization through multi-layer optimization.
Data Source
AI summary
A method, a device, and a non-transitory storage medium are described in which a mobile edge network-based intelligent routing service is provided. The intelligent routing service includes using default network devices to initially provide application services to end devices. The intelligent routing service further includes selecting and migrating the application services to a multi-layered mobile edge computing network when performance metrics associated with the application services are not satisfied. The intelligent routing service may determine whether performance metrics are satisfied based on performance metric information obtained from the serving nodes and the end devices.