Multi-Tiered MEC Network Service Provisioning
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Solution Overview
Problem
Multi-access Edge Computing (MEC) networks face challenges in efficiently managing network resources to meet performance metric requirements due to the high cost of MEC servers with limited resources at the edge and the latency issues of cloud servers located far from the edge.
Innovation Solution
Implementing a multi-tiered network environment with MEC servers deployed at different proximities to the network edge, including access, backhaul, and core networks, and using an end device service profile to select candidate networks and servers that minimize resource utilization while satisfying performance metrics such as latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If MEC servers are deployed at the network edge to reduce latency, then service response time is improved, but network resource cost increases and resource availability is limited
Solution Approach 1:
The patent segments the network into multiple tiers (edge, mid, core) with MEC servers deployed at different levels. This segmentation allows services to be distributed across multiple locations, reducing latency for edge-critical services while leveraging mid and core tier resources for less time-sensitive operations, thereby balancing both latency and resource availability.
Solution Approach 2:
The patent introduces a multi-dimensional network architecture with vertical tiering (edge, mid, core) and horizontal service migration capabilities. This dimensional expansion allows the system to select optimal server locations based on multiple factors including latency requirements and resource availability, resolving the contradiction between proximity and resource constraints.
2Loss of time
If MEC servers are deployed at the network edge to reduce latency, then service response time is improved, but resource cost increases
Solution Approach 1:
The patent implements dynamic service migration capabilities that allow services to move between edge, mid, and core tier MEC servers based on real-time conditions. This dynamic adjustment enables the system to place latency-critical services at the edge when needed while migrating less time-sensitive services to mid or core tiers to reduce resource costs, achieving optimal balance between latency and cost.
Solution Approach 2:
The patent changes the operational parameters of MEC servers across different tiers, with edge tier optimized for low latency operations and mid/core tiers optimized for resource efficiency. The system dynamically adjusts service placement based on latency requirements and resource availability parameters, resolving the contradiction between latency performance and resource cost.
3Quantity of substance
If cloud servers are used to provide abundant network resources, then resource availability is improved, but latency increases due to distance from network edge
Solution Approach 1:
The patent segments network resources across multiple tiers, with edge tier MEC servers providing low-latency services and mid/core tier servers providing abundant resources. This segmentation allows the system to leverage cloud-like resource availability at mid and core tiers while maintaining low latency through edge tier deployment for time-critical services.
Solution Approach 2:
The patent introduces mid-tier MEC servers as intermediaries between edge and core tiers. These mid-tier servers provide additional resource availability closer to the edge than core servers while incurring less latency than core tier deployment, serving as a mediator that balances resource availability and latency requirements.
Data Source
AI summary
A method, a device, and a non-transitory storage medium are described in which multi-tiered networks and resource utilization-based provisioning service is provided. A multi-tiered mobile edge computing network that includes multiple mobile edge computing networks that are multi-tiered based on distance from a network edge includes a network device that selects a location to provision an application service for an end device based on a total resource utilization value and a performance metric associated with one or multiple candidate mobile edge computing networks.