External Device MEC Selection for Edge Computing Load Management
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Solution Overview
Problem
Current network-based service provision systems lack dynamic and granular control over the selection of edge computing devices, such as Multi-Access/Mobile Edge Computing (MEC) devices, to meet varying service requirements and resource availability, leading to inefficient resource allocation and increased network processing loads.
Innovation Solution
Implement a system that allows external devices to dynamically select MECs based on factors like load, available services, UE location, and resource requirements, enabling offloading of MEC selection processes from the network and providing enhanced control and efficiency in resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the network centrally manages and selects MEC devices for service provision, then service delivery control is maintained, but network processing loads increase and system responsiveness decreases
Solution Approach 1:
The patent extracts the MEC selection and service provisioning decisions from the network core and relocates them to external devices. External devices now autonomously select suitable MEC devices based on service requirements, UE location, and resource availability, thereby reducing network processing loads while maintaining service delivery control through policy guidelines.
Solution Approach 2:
The patent introduces external devices as intermediaries between the network and MEC devices. These external devices act as mediators that make intelligent selection decisions based on multiple factors including service requirements, UE location, and MEC resource availability, thereby distributing the decision-making burden away from the network core.
2Ease of operation
If the network centrally manages and selects MEC devices for service provision, then service delivery control is maintained, but system adaptability and responsiveness decrease
Solution Approach 1:
The patent implements dynamic MEC selection where external devices can adaptively choose different MEC devices based on real-time conditions such as service requirements, UE location, and MEC resource availability. This dynamic approach enables the system to respond flexibly to changing service demands while the network maintains overall control through policy guidelines.
Solution Approach 2:
The patent enables local decision-making at external devices, allowing each external device to autonomously select the most appropriate MEC device based on local service requirements and conditions. This distributed intelligence improves system adaptability and responsiveness while the network maintains macro-level control through policy enforcement.
3Productivity
If MEC selection is performed by external devices based on multiple factors, then resource allocation efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where external devices autonomously perform MEC selection based on service requirements and resource availability. The system automatically matches services with appropriate MEC devices without requiring complex centralized coordination, thereby improving resource allocation efficiency while managing complexity through standardized interfaces and policy guidelines.
4Device complexity
If static service provisioning is used, then system simplicity is maintained, but resource utilization efficiency decreases
Solution Approach 1:
The patent transitions from static to dynamic service provisioning where MEC devices are selected based on real-time service requirements, UE location, and resource availability. This dynamic approach optimizes resource utilization efficiency by matching services with the most appropriate MEC devices at any given time, while the network maintains control through policy guidelines to manage system complexity.
Data Source
AI summary
A system described herein may provide a technique for the dynamic selection of edge computing devices, such as Multi-Access/Mobile Edge Computing devices (“MECs”), to provide services to User Equipment (“UEs”) based on factors such as MEC load, services and/or applications available or supported by particular MECs, UE location, service requirements, and/or other factors. One or more devices that are external to a network with which MECs are provided may be able to request services from a suitable MEC and/or identify a suitable MEC to provide such services. In this manner, control over the selection of particular MECs may be provided to devices or systems that are external to the network, thus providing an enhanced level of granular control and dynamism to such external devices or systems with respect to MEC selection.