Serverless GPU Function Workers for Bandwidth-Aware Concurrent Execution
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Solution Overview
Problem
Configuring a service to execute code on a GPU can be difficult due to infrastructure, scalability, and security concerns, especially in a serverless architecture.
Innovation Solution
A serverless architecture that allows for GPU cloud function execution without requiring users to manage infrastructure or configure services, utilizing a central controller and agents to deploy workers across clusters based on GPU availability and requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a service is configured to execute code on GPU, then GPU computing efficiency is improved, but service configuration complexity and infrastructure management burden increase
Solution Approach 1:
The patent introduces a serverless platform as an intermediary layer between users and GPU infrastructure. This platform includes a resource manager that automatically handles worker deployment, GPU allocation, and service configuration. Users simply submit code through API endpoints without needing to configure complex infrastructure, firewalls, or scalability settings. The platform mediates between the simple user interface and the complex underlying GPU computing infrastructure, resolving the contradiction between ease of use and computing efficiency.
Solution Approach 2:
The system implements self-service through automated worker provisioning and resource management. When code is submitted, the resource manager automatically detects GPU availability, provisions workers in appropriate cluster environments, and manages the entire execution lifecycle without human intervention. The system self-adjusts to workload demands, automatically scaling infrastructure as needed while maintaining security and performance standards, thereby eliminating the need for users to manually configure complex services.
2Adaptability or versatility
If infrastructure management is required for GPU execution, then control over computing resources is improved, but ease of operation deteriorates
Solution Approach 1:
The resource manager implements self-service by automatically detecting available GPUs in the environment, determining appropriate worker configurations, and provisioning infrastructure without user intervention. The system autonomously manages the entire lifecycle from worker creation to execution and termination. Users retain control over their code and data while the platform handles all infrastructure management tasks, achieving both ease of operation and resource adaptability.
Solution Approach 2:
The patent segments the system into distinct functional layers: a simple user interface layer for code submission, a resource management layer for infrastructure control, and an execution layer for GPU processing. This segmentation allows users to interact only with the simple interface while the resource management layer handles complex infrastructure decisions, thereby maintaining both ease of operation and adaptive resource control.
3Reliability
If workers are deployed across multiple cluster environments, then scalability and availability are improved, but system complexity increases
Solution Approach 1:
The resource manager implements a universal interface and standardized worker deployment mechanism that works across diverse cluster environments. Rather than creating complex environment-specific configurations, the system uses a unified approach where workers can be deployed to any suitable cluster through the same resource management logic. This multi-functionality allows the system to maintain high availability across multiple environments while keeping the management layer simple and consistent.
Solution Approach 2:
The resource manager acts as an intermediary that abstracts away the complexity of multi-environment deployment. It provides a standardized interface for worker provisioning that handles the intricacies of different cluster environments internally. Users interact with a single unified system rather than managing multiple complex environments directly, thereby achieving high availability across clusters without increasing perceived system complexity.
4Reliability
If security configurations are implemented for remote GPU access, then system security is improved, but ease of operation worsens
Solution Approach 1:
The system implements self-service security by automatically configuring firewalls, authentication mechanisms, and access controls when workers are deployed. The resource manager handles security configurations autonomously based on the deployment context, eliminating the need for users to manually set up complex security policies. Users simply submit their code while the system automatically establishes secure access channels, achieving both security and ease of operation.
Data Source
AI summary
Disclosed are systems and techniques for a cloud function worker for executing code using graphics processing units (GPUs) in a serverless architecture. The techniques include receiving, at a cloud function worker, a first cloud function execution request from a cloud function queue of a cloud function controller, executing a first instance of a code based on the first cloud function execution request using at least a graphics processing unit (GPU) of a cluster environment hosting the cloud function worker, determining first metrics of the GPU of the cluster environment, responsive to the first metrics of the GPU satisfying a bandwidth criterion, requesting a second cloud function execution request from the cloud function queue of the cloud function controller, and executing, concurrent with execution of the first instance of the code, a second instance of the code based on the second cloud function execution request.