Edge Node AI Service Orchestration via Platform Intermediary
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Solution Overview
Problem
The increasing deployment of Artificial Intelligence (AI) services on edge devices poses challenges in efficient management and delivery, particularly in real-time service responses, security, and privacy protection, as existing systems lack effective mechanisms for orchestrating and managing AI services across multiple edge nodes.
Innovation Solution
A service management method and platform that acquires monitoring information from edge nodes, selects available nodes, creates and distributes AI service transactions, and manages the lifecycle of these transactions, including initialization, orchestration, and failover, using AI service orchestration scripts and image acquisition devices, ensuring efficient and flexible transaction management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI services are deployed on multiple edge devices, then service coverage and availability are improved, but management complexity and orchestration difficulty increase
Solution Approach 1:
The patent introduces a service delivery platform as an intermediary between the service management platform and edge devices. This platform manages the deployment, orchestration, and lifecycle of AI services across multiple edge nodes, abstracting the complexity from the service management system while ensuring reliable service delivery. The service delivery platform handles container management, resource allocation, and coordination among edge devices.
Solution Approach 2:
The system divides the management architecture into distinct layers: service management platform (high-level orchestration), service delivery platform (middleware for deployment and coordination), and edge devices (execution layer). This segmentation allows each component to focus on specific tasks, reducing overall management complexity while maintaining service availability across distributed edge nodes.
2Productivity
If AI service orchestration is implemented across edge nodes, then service management efficiency is improved, but system complexity and resource requirements increase
Solution Approach 1:
The service delivery platform implements universal container-based service management that can handle multiple AI services across different edge nodes with varying resources. The platform provides multi-functional capabilities including service deployment, monitoring, scaling, and lifecycle management through a unified interface, improving management efficiency without proportionally increasing system complexity.
Solution Approach 2:
The system dynamically adjusts resource allocation and service configuration parameters based on edge node capabilities and service requirements. By changing parameters such as container resources, service replication factors, and orchestration policies, the system optimizes management efficiency while adapting to different system complexity levels and resource constraints.
3Speed
If real-time monitoring and distribution of AI services is implemented, then service response time is improved, but data transmission overhead and processing load increase
Solution Approach 1:
The service delivery platform performs preliminary actions by pre-loading AI service containers to edge nodes before they are needed, and maintaining ready-to-deploy service images. This advance preparation reduces real-time data transmission requirements when services need to be deployed or scaled, improving response time while minimizing ongoing transmission overhead.
Solution Approach 2:
The system uses container copying technology to efficiently replicate AI service deployments across edge nodes. Instead of transmitting full service configurations and models repeatedly, the platform copies optimized container images that already contain all necessary service components, reducing data transmission overhead while enabling rapid service deployment and real-time response.
Data Source
AI summary
A service management method, a platform, a service delivery system, and a computer storage medium. The service management method includes: using a service delivery platform to obtain edge node monitoring information, and selecting an available edge node from the obtained monitoring information; creating one or a plurality of transactions, each of the transactions including one or a plurality of artificial intelligence (AI) services; using the service delivery platform to issue the created transaction(s) to the available edge node. The embodiments of the present disclosure realize a cloud-native edge node management method, the transaction issuing mode is efficient and convenient, the transaction management mechanism is efficient and flexible, and the use of AI service orchestration ensures flexible construction, convenience and controllability of transactions.