Dynamic Endpoint Management for Heterogeneous ML Models

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Solution Overview

Problem

Existing infrastructure management systems for hosting machine learning models struggle with maximizing hardware utilization, leading to performance degradation and increased costs due to inefficient scaling and resource allocation.

Innovation Solution

Dynamic endpoint management for heterogeneous machine learning models, which allows for optimal placement and scaling of models across multiple hosts, using techniques such as load-aware routing and fine-tuned model placement to maximize resource utilization and performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of energy

If machine learning models are deployed across multiple hosts to improve hardware utilization, then resource efficiency increases, but system complexity increases

Engineering Contradiction:

Improvehardware utilization efficiencyVSAvoidsystem complexity

Core Design Contradiction:

Loss of energyVSDevice complexity

Solution Approach 1:

A centralized management system acts as an intermediary between multiple hosts and machine learning models, coordinating model placement, scaling, and resource allocation. This mediator abstracts the complexity of distributed system management while optimizing hardware utilization across hosts through intelligent routing and load balancing mechanisms.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system dynamically adjusts model placement and scaling across hosts based on real-time workload demands and resource availability. Models can be migrated between hosts, and scaling operations are performed automatically to maintain optimal hardware utilization without requiring static, pre-configured deployments.

Inventive Principle:

Principle #15Dynamics

2Adaptability or versatility

If dynamic scaling is implemented to handle varying workload demands, then system adaptability improves, but performance stability deteriorates

Engineering Contradiction:

Improveworkload adaptabilityVSAvoidperformance stability

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system continuously monitors workload demands, resource utilization, and model performance metrics, using this feedback to make informed scaling and placement decisions. This closed-loop control ensures that dynamic scaling operations maintain performance stability by adjusting resources based on actual system state rather than reactive changes.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary scaling operations in anticipation of workload changes by monitoring trends and predicting future resource needs. Models are pre-scaled or pre-positioned on hosts before demand spikes occur, ensuring performance stability is maintained during workload transitions rather than reacting after performance degradation begins.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250173183A1Dynamic endpoint management for heterogeneous machine learning models

Publication Date: 2025.05.29 AMAZON TECH INC

AI summary

Dynamic endpoint management is performed for heterogenous machine learning models. A placement event is detected for a machine learning model associated with a managed network endpoint. The managed network endpoint may provide access to different machine learning models via requests to invoke specified ones of the machine learning models received from clients of the machine learning service. A computing resource is selecting from the associated computing resources based on a determination that the computing resources satisfies a resource requirement for the machine learning model and the machine learning model is placed on the selected computing resource.