Hypergraph-Based IoT Edge Model Lifecycle Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional cloud-based environments for IoT systems are not responsive to changes near the data source, making it cumbersome and error-prone to manage the lifecycle of machine-learned models and data processors across large, complex, and changing IoT networks, hindering real-time data insights.
Innovation Solution
A hypergraph-based system for dynamic management of ML functions and modeling at the IoT Intelligent Edge, enabling efficient lifecycle management from creation to execution, with version management, resource assessment, and deployment of refined models across edge and core devices using a microservices architecture and containerized microservices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-based environments are used for managing analytic models, then centralized control and model deployment are achieved, but responsiveness to changes near data sources deteriorates and latency increases
Solution Approach 1:
The system segments the centralized cloud-based model management into distributed edge-based model management. Multiple edge devices independently manage their own analytic models and data processors, eliminating the single-point bottleneck at the cloud center. This segmentation enables local devices to respond immediately to data changes without waiting for cloud-based approval or deployment cycles.
Solution Approach 2:
The system adds a spatial dimension to model management by distributing capabilities across the network from a single cloud center to multiple edge locations. This dimensional shift from centralized to distributed architecture enables simultaneous model management at multiple network levels, improving responsiveness while maintaining centralized oversight through the hypergraph framework.
2Reliability
If manual tracking and deployment of multiple models with different frameworks is performed, then version management is achieved, but time consumption and error rates increase
Solution Approach 1:
The system creates a virtual copy of the entire model management state through hypergraph representation. Instead of manually tracking each model version across different frameworks, the hypergraph captures the complete state of all analytic functions, their versions, dependencies, and relationships in a unified data structure. This virtual copy enables automated version management, comparison, and deployment without manual intervention.
Solution Approach 2:
The hypergraph-based framework provides a universal management interface that works across multiple ML frameworks and model types. Rather than requiring separate manual tracking processes for each framework, the system uses a single unified hypergraph structure that can represent and manage diverse analytic functions uniformly, reducing both time consumption and error rates.
3Adaptability or versatility
If complex networks with multiple data processors and ML models are deployed, then analytical capability is improved, but system complexity and difficulty of management increase
Solution Approach 1:
The hypergraph structure serves as an intermediary layer between the complex deployed models and the management interface. It captures the state, dependencies, and relationships of all analytic functions in a unified representation, simplifying management operations. The hypergraph acts as a mediator that translates complex multi-framework model relationships into manageable virtual nodes and edges, reducing the perceived system complexity.
Solution Approach 2:
The system uses dynamic hypergraph representation that automatically adapts to changes in the deployed network. As models are added, removed, or modified, the hypergraph structure dynamically updates to reflect the current state, maintaining an accurate representation without requiring manual reconfiguration. This dynamic adaptation simplifies management of complex evolving networks.
Data Source
AI summary
The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.