AI Manager for Distributed Resource Allocation
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Solution Overview
Problem
The deployment and management of artificial intelligence (AI) models in distributed environments often require specialized expertise, leading to sub-optimal deployments, resource inefficiencies, and slow adoption due to the complexity of understanding both AI models and infrastructure requirements.
Innovation Solution
An AI manager system that determines and optimizes the deployment and configuration of AI resources, including models and hardware accelerators, to ensure compatibility, efficiency, and access control, allowing users to utilize AI resources without needing detailed knowledge of AI models or infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If operations teams manually manage AI model deployments and resource allocation, then they can control infrastructure requirements, but deployment efficiency decreases and resource utilization becomes sub-optimal due to lack of AI model expertise
Solution Approach 1:
An AI manager system is introduced as an intermediary between operations teams and AI models. The AI manager automatically determines resource requirements, selects appropriate hardware accelerators, and manages deployment configurations, eliminating the need for operations teams to have AI expertise while maintaining control over infrastructure resources.
Solution Approach 2:
The system enables AI models to self-manage their resource allocation through the AI manager. The AI manager automatically determines compute, memory, and storage requirements for each AI model and provisions appropriate resources without manual intervention, allowing rapid deployment and dynamic resource optimization.
2Reliability
If specialized expertise is required for both AI models and infrastructure, then deployment accuracy improves, but adoption speed decreases due to the scarcity of personnel with dual expertise
Solution Approach 1:
The system separates concerns by dividing responsibilities between the AI manager and operations teams. The AI manager handles AI-specific decisions (model requirements, resource selection, configuration) while operations teams focus on infrastructure management. This segmentation allows each component to specialize without requiring dual expertise, improving both accuracy and adoption speed.
3Loss of time
If manual resource allocation is used, then resource compatibility can be verified, but deployment time increases and downtime is minimized only with automated management
Solution Approach 1:
The AI manager performs preliminary actions by pre-determining resource requirements and pre-configuring hardware accelerators before AI model deployment. This advance preparation eliminates compatibility verification delays during actual deployment, reducing deployment time while managing complexity through automated workflows.
4Productivity
If AI resources are not optimized, then resource availability increases, but throughput decreases and resource utilization becomes inefficient
Solution Approach 1:
The AI manager implements feedback mechanisms to continuously monitor AI model performance and resource utilization. Based on this feedback, the system dynamically adjusts resource allocation, selects optimal hardware accelerators, and reconfigures deployments to maximize throughput and efficiency while adapting to changing workloads and availability.
Data Source
AI summary
Approaches presented herein provide for the management of artificial intelligence (AI)-related resources in a distributed resource environment, such as may be used to support accelerated machine learning (ML) applications on behalf of different users. Management functionality can be provided using an AI manager, such as a management service, that can determine the requirements, capabilities, and limitations of various available AI-related components, such as those of a plurality of AI models, engines, and accelerators, as well as the hardware (e.g., graphics processing units (GPUs)) that run or make up these AI-related resources. An AI manager can determine a selection and configuration of resources that is not only appropriate for use with a specific AI model, but that can also be optimized for factors such as throughput, resource utilization, and inference latency. An AI manager can ensure compatibility of resources and configuration, and can enforce access control to models and data.