ML Model Selection via Complexity Metrics for Resource Constraints
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Solution Overview
Problem
Current methods for selecting machine learning models in resource-constrained environments, such as base stations and IoT systems, are inefficient due to high costs and complexity, as they do not adequately consider resource constraints and latency requirements, leading to suboptimal performance and increased latency.
Innovation Solution
A method and apparatus that retrieve and calculate the complexity of machine learning models from a model store, determining suitable models based on the execution environment's resource constraints and latency requirements, ensuring compatibility and balanced performance without overloading the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If field trials are performed to determine ML model impacts, then model suitability is improved, but cost and time consumption increase
Solution Approach 1:
The patent pre-calculates and stores complexity metrics for multiple ML models in a model store before actual deployment needs arise. This preliminary preparation eliminates the need for time-consuming field trials, as the system can directly query pre-analyzed model characteristics to make deployment decisions.
Solution Approach 2:
The patent creates a virtual representation of the execution environment's resource constraints and uses this copy to simulate and evaluate ML model performance through complexity calculations. This virtual modeling approach replaces physical field trials while maintaining evaluation accuracy.
2Measurement precision
If testbed execution is used to evaluate ML models, then performance data is collected, but manual intervention and system complexity increase
Solution Approach 1:
The patent replaces the mechanical/manual testbed execution system with an automated computational approach. Instead of physically configuring hardware testbeds and manually running tests, the system uses software-based complexity calculations that automatically evaluate ML models against resource constraints, eliminating manual intervention while maintaining measurement capability.
Solution Approach 2:
The system performs self-evaluation by automatically calculating complexity metrics for ML models based on their inherent characteristics and the execution environment's resource constraints. This self-service mechanism eliminates the need for external manual testing while providing continuous performance assessment.
3Reliability
If complex ML models are deployed to improve task accuracy, then task performance is improved, but resource constraints are violated
Solution Approach 1:
The patent transforms the ML model selection problem from a qualitative assessment to a quantitative parameter-based evaluation. By calculating specific complexity parameters (computational complexity, memory requirements, data sampling needs) and comparing them against resource constraint parameters, the system objectively determines the optimal model that maximizes accuracy within resource limits.
Solution Approach 2:
The system dynamically adapts ML model selection based on real-time resource availability and task requirements. Rather than using a fixed model, the system can adjust which model to deploy based on current execution conditions, balancing accuracy needs with available resources through automated complexity-based selection.
4Adaptability or versatility
If multiple ML models with varying complexities are available, then task versatility is improved, but model selection difficulty increases
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors execution environment resource constraints and uses this feedback to automatically select appropriate ML models. The model store stores complexity metrics that provide immediate feedback on model suitability, enabling automated selection without manual intervention.
Solution Approach 2:
The patent creates a universal model store that serves multiple functions: storing ML models, calculating complexity metrics, comparing models against resource constraints, and providing selection recommendations. This multi-functional system handles diverse ML models and various resource constraint scenarios through a single unified approach, simplifying the selection process.
Data Source
AI summary
Embodiments herein disclose a method for selecting a machine learning model to be deployed in an execution environment having resource constraints. The method comprises receiving, by an apparatus, a request for a machine learning model solving a task T using a feature set F. Further, the method includes retrieving, from a model store, a first set of machine learning models that solves the task T using at least a subset of features F. The complexity of each machine learning model in the first set of machine learning models is calculated. The method includes determining, from the first set of machine learning models, at least one suitable machine learning model to be deployed, wherein the determining is based on the calculated complexity and the resource constraints of the execution environment.