Load-Aware ML Endpoint Routing Across Heterogeneous Hosts
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Solution Overview
Problem
Managing multiple machine learning models across heterogeneous infrastructure is challenging due to varying hardware capabilities and workload variations, leading to inefficient resource utilization, performance degradation, and increased costs.
Innovation Solution
Implementing dynamic endpoint management and load-aware routing techniques to optimize the placement and distribution of machine learning models across hosts with varying hardware, using model-specific scaling policies and load-aware routing to ensure optimal performance and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple machine learning models are deployed across heterogeneous infrastructure, then model availability and accessibility are improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements a universal routing layer that manages multiple heterogeneous machine learning models through a common network endpoint. This routing layer dynamically directs requests to appropriate models based on current system state, allowing a single infrastructure to efficiently support multiple model types and workloads without requiring separate dedicated endpoints for each model.
Solution Approach 2:
The system employs dynamic load-aware routing that continuously monitors and adjusts request distribution based on real-time workload conditions, model performance metrics, and resource availability. This dynamic adaptation enables the system to optimize resource utilization while maintaining high model availability across heterogeneous infrastructure.
2Productivity
If machine learning models are distributed across heterogeneous hosts, then system scalability is improved, but performance consistency deteriorates
Solution Approach 1:
The routing layer incorporates feedback mechanisms that monitor model performance metrics, response times, and resource utilization across heterogeneous hosts. This feedback information is used to dynamically adjust routing decisions, ensuring that requests are directed to models that can deliver consistent performance regardless of the underlying hardware variations, thus maintaining performance consistency while enabling system scalability.
Solution Approach 2:
The system dynamically changes routing parameters and load distribution strategies based on real-time performance monitoring. By adjusting routing parameters such as weight factors, threshold values, and selection criteria according to current system conditions, the system maintains performance consistency across heterogeneous infrastructure while continuing to scale.
3Loss of energy
If dynamic endpoint management is implemented, then hardware utilization is improved, but system complexity increases
Solution Approach 1:
The patent introduces a routing layer as an intermediary component that sits between the client applications and the heterogeneous machine learning models. This intermediary manages the complexity of dynamic endpoint management, load balancing, and resource optimization internally, while presenting a simplified interface to external users. The routing layer handles hardware utilization optimization without requiring end users to deal with the underlying system complexity.
Data Source
AI summary
Load aware routing is performed for requests to managed network endpoints for heterogeneous machine learning models. A request to generate an inference is received via a managed network endpoint that invokes a specified machine learning model. Workloads of the different hosts for respective replicas of the machine learning model are evaluated to select one of the hosts to perform the request.