Load-Aware Routing for Heterogeneous ML Endpoint Inference
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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 inefficiencies in resource utilization and performance degradation.
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 intelligent routing to maximize utilization and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning models are deployed across heterogeneous infrastructure, then model diversity and functionality are improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements a unified endpoint management system that consolidates access to multiple heterogeneous machine learning models through a single endpoint. The system dynamically routes requests to appropriate models based on workload characteristics, combining multiple model functionalities into one universal access point. This resolves the contradiction by providing model diversity through a unified management layer that optimizes resource allocation across heterogeneous infrastructure.
Solution Approach 2:
The system employs dynamic routing and load balancing mechanisms that adaptively distribute workloads across different machine learning models based on real-time conditions. Models are dynamically activated or deactivated based on demand, and request routing is adjusted in response to workload variations. This dynamic management optimizes resource utilization while maintaining the ability to support diverse model types.
2Productivity
If dynamic endpoint management is implemented, then hardware utilization is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary endpoint management layer that sits between clients and the heterogeneous machine learning models. This intermediary handles the complexity of dynamic routing, load balancing, and resource allocation, while presenting a simplified interface to clients. The intermediary absorbs the system complexity, allowing hardware utilization to be optimized without directly increasing client-facing complexity.
Solution Approach 2:
The endpoint management system implements self-service mechanisms through automated routing decisions and dynamic load balancing algorithms. The system autonomously manages workload distribution and model activation without requiring manual intervention, thereby optimizing hardware utilization while keeping operational complexity manageable through automation rather than manual processes.
3Reliability
If load-aware routing is used, then performance consistency is improved, but routing complexity increases
Solution Approach 1:
The patent implements load-aware routing that incorporates feedback mechanisms to monitor system state and adjust routing decisions accordingly. The routing system continuously receives feedback about model performance, workload characteristics, and resource availability, using this information to maintain consistent performance across diverse model deployments. This feedback-driven approach achieves performance consistency while managing routing complexity through systematic decision-making.
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.