AI Model Deployment for Edge-Cloud Service Optimization
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Solution Overview
Problem
Existing analysis systems face challenges in efficiently providing complex services due to insufficient local resources, requiring data streams to be sent to remote systems, which can lead to resource consumption and performance issues.
Innovation Solution
An application management system is introduced to integrate and deploy artificial intelligence (AI) models across various network devices, enabling dynamic task allocation between edge and cloud environments. This system selects and deploys AI models based on monitoring configurations and service requirements, optimizing resource usage and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data streams are sent to remote systems for processing, then service capability is improved, but resource consumption and latency increase
Solution Approach 1:
The system segments service processing into multiple functions that can be selectively deployed to different locations (edge devices vs. cloud). Not all data streams need to be sent to remote systems - only those requiring complex AI processing. Simple processing can be done locally at edge devices, reducing unnecessary resource consumption while maintaining service capability where needed.
Solution Approach 2:
The system dynamically determines where to process data based on service requirements and resource availability. The application management system can adaptively route data streams to appropriate processing locations in real-time, optimizing the balance between service capability and resource consumption rather than using a fixed approach.
2Adaptability or versatility
If data streams are sent to remote systems for processing, then service capability is improved, but processing latency increases
Solution Approach 1:
By segmenting processing functions and deploying them at edge locations closer to data sources, the system reduces the distance data must travel for processing. Time-sensitive services can be handled at edge devices with minimal latency, while less time-sensitive processing occurs in the cloud.
Solution Approach 2:
The system dynamically routes data streams based on latency requirements and service needs. Critical real-time data can be directed to nearby edge devices for immediate processing, while non-critical data can be sent to remote systems, optimizing latency performance without sacrificing service capability.
3Reliability
If AI models are deployed across multiple network devices, then service robustness is improved, but system complexity increases
Solution Approach 1:
The application management system provides a universal interface for deploying and managing AI models across diverse network devices. Rather than increasing complexity, the system abstracts the underlying device diversity through standardized deployment mechanisms, allowing robust multi-device AI model execution while maintaining manageable system architecture.
Solution Approach 2:
The application management system acts as an intermediary layer between AI model deployment and individual network devices. This mediator handles the complexity of model distribution, versioning, and coordination across multiple devices, presenting a simplified interface that improves service robustness without exposing the increased system complexity to users.
Data Source
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AI summary
In some implementations, a system may receive, from an analysis system, a data stream associated with monitoring an environment. The system may process, using a first model, the data stream to detect an object associated with a service provided by an application. The system may analyze a monitoring configuration of the analysis system. The system may determine, based on processing the data stream, service data associated with providing the service in association with the object. The system may configure, based on the monitoring configuration, an edge device to access the data stream and host a second model to process the service data according to the data stream. The system may receive, from the edge device, a service output associated with providing the service in association with the object. The system may perform an action associated with the service output and the object.