Autonomous Cloud-Node Scoping for ML Configurations
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Solution Overview
Problem
Implementing machine learning software applications with cloud container technology faces challenges in scalability due to complex interrelationships between memory, GPU/CPU power, and sensor stream parameters, leading to high overhead costs and inefficiencies in achieving low-latency and high-throughput performance for big-data streaming prognostics.
Innovation Solution
An autonomous cloud-node scoping framework using a nested-loop Monte Carlo-based simulation to autonomously scale machine learning use cases across various cloud CPU-GPU configurations, providing automated scoping assessments for optimal container sizing and performance estimation based on signal and observation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud container technology is used for machine learning applications, then deployment flexibility and scalability are improved, but performance optimization becomes difficult due to complex interrelationships between memory, GPU/CPU power, and sensor stream parameters
Solution Approach 1:
The system performs self-service by automatically generating recommended processor-memory configurations through simulation and analysis, eliminating the need for manual configuration by data scientists or consultants. The framework autonomously scales machine learning use cases across cloud CPU-GPU configurations and provides automated scoping assessments for optimal container sizing.
Solution Approach 2:
The framework performs preliminary action by conducting nested-loop Monte Carlo-based simulations before actual deployment to determine optimal configurations. This advance analysis identifies the best processor-memory configurations for specific machine learning workloads, avoiding trial-and-error methods and enabling informed deployment decisions.
2Adaptability or versatility
If manual trial-and-error methods are used to determine optimal configurations, then customization is possible, but time consumption and costs increase significantly
Solution Approach 1:
The system replaces manual mechanical trial-and-error configuration methods with automated computational simulation and analysis. The framework uses nested-loop Monte Carlo-based simulation to automatically evaluate multiple configurations and generate recommendations, substituting human effort with algorithmic optimization.
Solution Approach 2:
The framework systematically changes parameters such as memory size, processor count, GPU configuration, and sensor stream parameters across multiple simulation iterations. By varying these parameters through nested loops and Monte Carlo methods, the system identifies optimal configurations without manual intervention.
3Productivity
If higher memory and compute power are provisioned, then throughput and latency performance improve, but operational costs increase
Solution Approach 1:
The framework analyzes the relationship between resource parameters (memory, processor, GPU) and performance metrics (throughput, latency) by systematically varying these parameters in simulations. This enables identification of the optimal parameter combination that achieves required performance at minimum cost.
Solution Approach 2:
The system incorporates feedback by measuring actual performance metrics from simulated or test runs and using this information to refine configuration recommendations. The framework evaluates throughput and latency outcomes and adjusts resource allocation recommendations accordingly, ensuring cost-effective performance optimization.
Data Source
AI summary
Systems, methods, and other embodiments associated with autonomous cloud-node scoping for big-data machine learning use cases are described. In some example embodiments, an automated scoping tool, method, and system are presented that, for each of multiple combinations of parameter values, (i) set a combination of parameter values describing a usage scenario, (ii) execute a machine learning application according to the combination of parameter values on a target cloud environment, and (iii) measure the computational cost for the execution of the machine learning application. A recommendation regarding configuration of central processing unit(s), graphics processing unit(s), and memory for the target cloud environment to execute the machine learning application is generated based on the measured computational costs.