ML Model Sharing via Reverse Mount for Edge Inference
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Solution Overview
Problem
IoT devices face challenges in improving performance, efficiency, and security when utilizing machine learning resources, particularly in sharing machine learning models due to resource constraints and security vulnerabilities in edge computing environments.
Innovation Solution
A method for sharing machine learning models between application pods and ML inference pods that allows the ML inference pod to load the model directly from the application pod's namespace without copying it to local storage, using a reverse mount approach and hashing to ensure uniqueness, thereby preventing duplicate models and enhancing security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are copied to local storage in ML inference pods, then model availability and inference performance are improved, but storage requirements and device complexity increase
Solution Approach 1:
The patent extracts the machine learning model from the application pod's namespace and makes it accessible to the ML inference pod without copying to local storage. The model is pulled directly from the shared namespace through the reverse mount, eliminating the need for duplicate local copies while maintaining inference performance.
Solution Approach 2:
The reverse mount of the application pod's namespace creates a universal access mechanism where the ML inference pod can load models directly from the shared namespace. This multi-functional approach serves both as model storage and model serving, eliminating the need for separate local storage infrastructure.
2Productivity
If machine learning models are shared across application pods, then resource efficiency is improved, but security risks and model visibility vulnerabilities increase
Solution Approach 1:
The reverse mount mechanism acts as an intermediary that controls access to the application pod's namespace. Instead of direct access that would expose models to all pods, the reverse mount creates a controlled interface where only the designated ML inference pod can access the model, maintaining security while enabling sharing.
Solution Approach 2:
The patent applies local quality by making the namespace reverse mount specific to individual ML inference pods. Each pod receives a tailored access configuration that grants model access without exposing other pods' models, ensuring that security boundaries are maintained while resource efficiency is improved.
3Ease of manufacture
If machine learning models are loaded from cloud storage, then model updates are simplified, but inference latency and cloud dependency increase
Solution Approach 1:
The model is loaded into the application pod's namespace in advance as part of the application deployment. This preliminary action ensures the model is already available in the edge environment before inference is needed, eliminating cloud storage dependency and reducing inference latency while maintaining easy update capabilities through standard pod updates.
Solution Approach 2:
The ML inference pod loads the model directly from the application pod's namespace without requiring cloud storage intervention. This self-service approach enables the edge device to autonomously access and load models from local namespace, eliminating cloud dependency and reducing inference latency.
Data Source
AI summary
The present disclosure relates generally to systems, devices and/or processes for sharing machine learning models among components of a computing environment.