GPU Resource Orchestration for Multi-Tenant Isolation and Scaling
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Solution Overview
Problem
Current GPU resource management systems lack robust support for multi-tenancy, self-service APIs, and dynamic partitioning, leading to inefficient resource provisioning and suboptimal use of hardware in high-performance computing environments, particularly in multi-tenant settings.
Innovation Solution
An intent-based orchestration system that interprets high-level user intents to dynamically allocate GPU resources, considering cost, workload type, and geographic preferences, using an orchestrator to translate and provision resources efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If GPU resources are shared across multiple tenants to maximize resource utilization, then resource utilization improves, but security and performance isolation deteriorate
Solution Approach 1:
The patent segments GPU resources into isolated partitions for different tenants using virtualization techniques. Each tenant receives dedicated virtual GPU instances that are logically separated, ensuring security and performance isolation while maintaining high overall resource utilization through shared physical hardware.
Solution Approach 2:
The patent introduces an intermediary orchestration layer that manages resource allocation between multiple tenants and the physical GPU hardware. This intermediary enforces security policies, monitors performance, and ensures isolation while enabling efficient shared access to GPU resources across multiple users.
2Measurement precision
If manual provisioning is used for each individual workload, then resource allocation precision improves, but operational overhead and time consumption increase
Solution Approach 1:
The patent implements self-service automated provisioning systems that enable workloads to automatically request and allocate required GPU resources without manual intervention. The system precisely determines resource requirements through intelligent algorithms while eliminating operational overhead and reducing provisioning time.
Solution Approach 2:
The patent performs preliminary resource allocation and configuration actions automatically before workloads are deployed. The system pre-configures GPU resources, sets up isolation policies, and prepares execution environments in advance, eliminating manual provisioning steps and reducing deployment time while maintaining precise resource allocation.
3Device complexity
If static resource estimation methods are used, then system complexity is reduced, but resource utilization efficiency deteriorates due to underutilization or over-provisioning
Solution Approach 1:
The patent implements dynamic resource estimation that adapts to changing workload requirements in real-time. The system continuously monitors workload characteristics, adjusts resource allocation accordingly, and optimizes GPU utilization without requiring complex manual configuration. This dynamic approach maintains reasonable system complexity while dramatically improving resource utilization efficiency.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor actual resource usage and performance metrics, then use this information to automatically adjust resource allocation. The system learns from workload patterns and optimizes resource distribution dynamically, improving utilization efficiency while keeping system complexity manageable through automated control loops.
Data Source
AI summary
A system (106) and method (400) for intent-based orchestration of graphics processing unit (GPU) resources are disclosed. The method (400) involves receiving one or more high-level intents from one or more users (102), wherein the one or more high-level intents indicate the GPU-resource requirement of the one or more users (102). The one or more high-level intents are interpreted to derive contextual meaning associated with user-defined requirements. Based on the interpreted intents, the one or more high-level intents are translated into a predefined one or more GPU resources (210). The translated one or more GPU resources (210) are then provisioned in isolated manner to fulfil the GPU-resource requirement of the one or more users (102) such that intent-based orchestration of the GPU resources is achieved.