Geo-distributed Edge Cloud Service Deployment Platform
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Solution Overview
Problem
Telecommunication networks face challenges in efficiently deploying client services in mobile edge infrastructure due to the unique constraints of geo-distributed edge clouds, including varying resource capabilities and non-uniform distribution, which differ from centralized cloud service deployments.
Innovation Solution
A platform with a master node in a central cloud and agent nodes in geo-distributed edge clouds that tracks resources and facilitates service deployment based on customized requirements such as distance and latency, allowing for on-demand deployment of applications across edge clouds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If services are deployed in centralized cloud infrastructure, then resource utilization is improved, but latency and bandwidth consumption increase
Solution Approach 1:
The patent segments the centralized cloud infrastructure into distributed edge cloud nodes deployed across multiple geographic locations. This segmentation allows services to be executed closer to end users, reducing latency while maintaining efficient resource utilization through the collective pool of edge resources.
Solution Approach 2:
The patent introduces a spatial dimension to service deployment by distributing cloud infrastructure across geographic locations rather than concentrating it in a single data center. This dimensional change enables proximity-based service delivery, reducing transmission latency while preserving resource efficiency through coordinated multi-node operation.
2Loss of time
If edge clouds are used for service deployment, then latency is reduced, but resource capability uniformity deteriorates
Solution Approach 1:
The patent applies local quality by allowing each edge cloud node to operate with its own specific resource characteristics and capabilities rather than requiring uniform hardware. The system adapts service deployment to match local resource qualities, selecting appropriate nodes based on their specific capabilities while maintaining low latency through geographic distribution.
Solution Approach 2:
The patent changes the parameter of resource capability from a fixed uniform specification to a dynamic, node-specific attribute. The service deployment system adjusts deployment decisions based on varying resource parameters of different edge nodes, optimizing both latency performance and resource utilization efficiency.
3Adaptability or versatility
If client services are processed on client devices, then service customization is improved, but device workload and bandwidth consumption increase
Solution Approach 1:
The patent extracts resource-demanding computing components from client devices and relocates them to edge cloud nodes. This extraction maintains service customization capabilities while significantly reducing the computational workload and power consumption required by client devices, as well as conserving radio access network bandwidth.
Data Source
AI summary
A processing system including at least one processor may receive a request from a client device for a deployment of a client service to a mobile edge infrastructure of a telecommunication network, the mobile edge infrastructure including host devices, and determine at least one requirement for the client service, including at least one of: a distance requirement, comprising a maximum distance between the client device and a candidate host device for deploying the service, or a latency requirement, comprising a maximum latency between the client device and the candidate host device for deploying the service. The processing system may further determine at least one available host device of the mobile edge infrastructure that meets the requirements, select a host device from among the at least one available host device to run the client service, and instruct the client device to connect to the host device that is selected.