Platform Independent GPU Profiles for Virtual Machine Placement
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Solution Overview
Problem
The inefficient utilization of GPU resources in cloud-based or on-premise computing environments due to the need for manual configuration of virtual machines with platform-specific NVIDIA GRID vGPU profiles, which limits migration and availability of resources across heterogeneous graphics card environments, and restricts features like dynamic resource scheduling.
Innovation Solution
A Graphic Processing Unit Virtual Machine Placement Manager (GVPM) generates platform-independent GPU profiles based on actual graphics computing requirements, allowing virtual machines to be assigned to the most suitable hosts with compatible GPUs, regardless of make or model, and prioritizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If platform-specific NVIDIA GRID vGPU profiles are used for virtual machine configuration, then graphics processing resources can be allocated to virtual machines, but migration of virtual machines between hosts with different graphics cards is restricted and resource utilization efficiency decreases
Solution Approach 1:
The patent creates a universal GPU profile format that can represent graphics processing resources across different GPU platforms (NVIDIA, AMD, Intel). Instead of requiring separate platform-specific profiles, a single profile structure can describe resource characteristics that are platform-agnostic, enabling virtual machines to be migrated between hosts with different GPU vendors while maintaining compatibility. The profile includes abstracted resource descriptors that capture essential GPU capabilities without being tied to a specific manufacturer's implementation.
Solution Approach 2:
The patent transforms the configuration approach from using vendor-specific profile parameters to using standardized, abstracted parameters that describe GPU resource characteristics. By changing the parameter representation from platform-specific to platform-independent, the system enables flexible resource allocation and virtual machine migration across heterogeneous GPU environments while maintaining precise resource control.
2Ease of operation
If manual configuration of virtual machines with specific GPU profiles is required, then graphics processing can be provided to virtual machines, but operational costs increase and flexibility decreases
Solution Approach 1:
The patent implements self-service functionality where the GPU profile management system automatically matches virtual machine resource requirements with available GPU resources on host systems. Instead of requiring administrators to manually configure each virtual machine with specific GPU profiles, the system autonomously selects appropriate GPU resources based on the virtual machine's declared requirements and the available inventory, significantly reducing operational complexity while maintaining flexibility.
Solution Approach 2:
The patent introduces an intermediary layer between virtual machine requests and physical GPU resources. This intermediary profile system translates high-level virtual machine requirements into specific GPU resource allocations without requiring direct manual configuration. The intermediary automatically handles the complexity of matching virtual machine needs with available GPU capabilities across different platforms, simplifying the operational interface while managing underlying complexity.
3Productivity
If virtual machines are bound to specific GPU profiles, then graphics processing resources can be assigned, but dynamic resource scheduling and load balancing features become unavailable
Solution Approach 1:
The patent enables dynamic GPU resource allocation by decoupling virtual machine configurations from static, platform-specific GPU profiles. Instead of binding virtual machines to fixed profiles, the system uses abstracted, platform-independent resource descriptors that allow the GPU resources to be dynamically reassigned based on workload demands, host availability, and optimization goals. This dynamic approach enables load balancing and resource scheduling features while maintaining efficient resource allocation.
Data Source
AI summary
Disclosed are various examples for platform independent graphics processing unit (GPU) profiles for more efficient utilization of GPU resources. A virtual machine configuration can be identified to include a platform independent graphics computing requirement. Hosts can be identified as available in a computing environment based on the platform independent graphics computing requirement. The virtual machines can be migrated and placed to maximize usage the total memory of GPU resources of the hosts.