GPU Serverless Orchestration with Queued Multi-Cluster Workers
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Solution Overview
Problem
Configuring a service to execute code on a graphics processing unit (GPU) is difficult due to infrastructure, scalability, and security concerns, especially in a serverless architecture.
Innovation Solution
A serverless architecture that allows for the execution of GPU cloud functions without requiring users to manage infrastructure or services, utilizing a central controller and agents to deploy workers across clusters, managing queues for execution requests, and ensuring secure communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a service is configured to execute code on GPU for remote clients, then compute efficiency is improved, but infrastructure complexity and security concerns increase
Solution Approach 1:
The patent introduces a serverless platform as an intermediary layer between remote clients and GPU resources. This platform abstracts the complex infrastructure management, handling worker deployment, queue management, and resource allocation automatically. Clients interact with simple APIs while the platform manages the underlying GPU cluster, thus maintaining compute efficiency while reducing infrastructure complexity for users.
Solution Approach 2:
The system implements self-service through automated worker deployment and management. The serverless platform automatically provisions workers, manages their lifecycle, and allocates GPU resources without requiring manual configuration by users. This automation eliminates the need for users to directly manage complex GPU infrastructure while maintaining high compute efficiency.
2Adaptability or versatility
If GPU resources are made accessible to remote clients, then computational capability is improved, but security concerns increase
Solution Approach 1:
The serverless platform acts as a security intermediary, positioning itself between remote clients and GPU workers. It implements authentication, authorization, and secure communication protocols, thereby enabling clients to access GPU computational capability while maintaining security through centralized control and monitoring.
Solution Approach 2:
The system implements feedback mechanisms through queue management and worker status monitoring. Execution requests are queued and processed in a controlled manner, with the platform receiving feedback from workers about their status and capacity. This feedback loop enables secure resource allocation and prevents unauthorized or malicious access while maintaining computational capability.
3Productivity
If workers are deployed to handle execution requests, then processing capacity is improved, but vulnerability to overwhelming requests increases
Solution Approach 1:
The system performs preliminary actions by pre-deploying workers and pre-allocating GPU resources before execution requests arrive. Workers are registered and made available in advance, with their capabilities and status pre-configured. This preliminary preparation enables the system to handle incoming requests efficiently while maintaining stability through controlled resource allocation.
Solution Approach 2:
The queue management system provides beforehand cushioning by buffering execution requests before they are processed by workers. When request volume exceeds worker capacity, the queue absorbs the excess load, preventing system overload and maintaining stability. This cushioning mechanism protects workers from overwhelming requests while preserving processing capacity.
Data Source
AI summary
Disclosed are systems and techniques for a cloud function controller for executing code using graphics processing units (GPUs) in a serverless architecture. The techniques include maintaining, at a cloud function controller, a plurality of cloud function queues for a plurality of workers in a plurality of cluster environments. Each cluster environment hosts an agent that communicates with the cloud function controller and has GPU resources accessible to a subset of the plurality of workers. The techniques include storing a first cloud function execution request of an entity in a first queue of the plurality of cloud function queues, receiving a first cloud function execution result corresponding to the first cloud function execution request of the entity from a first worker of the plurality of workers in a first cluster environment of the plurality of cluster environments, and causing the first cloud function execution result to be provided to the entity.