ML Orchestration Module for Dynamic Network Resource Redistribution
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Solution Overview
Problem
Machine learning systems deployed on distributed networks often fail to respond effectively to changing network conditions due to suboptimal initial deployment and delayed redeployment, leading to performance gaps in meeting stringent requirements of IoT applications, particularly in latency, throughput, and real-time processing demands.
Innovation Solution
A machine learning orchestration module dynamically adjusts the depth and deployment of ML functions across cloud, fog, and edge network levels, leveraging different processing resources like CPUs, FPGAs, GPUs, and TPUs, to optimize performance and resource allocation based on real-time performance data and requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning functions are initially deployed on distributed networks to optimize efficiency, then network resource efficiency is improved, but the system becomes unresponsive to rapidly evolving application requirements and network conditions
Solution Approach 1:
The patent implements dynamic orchestration that continuously monitors performance data and automatically adjusts the deployment, configuration, and redistribution of machine learning functions across the distributed network. This dynamic approach allows the system to adapt to changing network conditions and application requirements in real-time, resolving the contradiction between initial deployment efficiency and ongoing adaptability.
2Adaptability or versatility
If machine learning functions are redeployed to meet changing requirements, then adaptability is improved, but redeployment delays cause the system to miss real-time performance optimization opportunities
Solution Approach 1:
The orchestration system operates continuously, monitoring performance metrics and executing adjustments without interruption. This continuous operation eliminates redeployment delays by maintaining an ongoing optimization process that responds immediately to changing conditions, ensuring both adaptability and real-time performance optimization.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where performance data is continuously collected, analyzed, and used to trigger automatic adjustments. This feedback-driven approach ensures that redeployment decisions are made based on real-time conditions, minimizing delay while maximizing adaptability to application requirements.
3Device complexity
If machine learning functions are statically deployed, then deployment complexity is reduced, but the system cannot meet stringent performance requirements for latency and throughput in IoT applications
Solution Approach 1:
The patent segments machine learning functions into modular components that can be independently deployed, monitored, and adjusted across the distributed network. This segmentation allows the orchestration system to manage complexity through modularization while maintaining the ability to meet stringent performance requirements by optimizing individual function placements based on real-time conditions.
Data Source
AI summary
Techniques for orchestrating a machine learning (ML) system on a distributed network. Determined performance levels for a ML system, determined from performance data received from the distributed network, are compared to performance requirements from the ML system. An orchestration module for the ML system then determines adjustments for the ML system that will improve the performance of the ML system and executes the adjustments for the ML system.