Virtual GPU Placement Optimization in Provider Networks
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Solution Overview
Problem
Managing and scaling virtualized graphics processing in distributed systems is complex due to increased scale and scope, requiring efficient resource provisioning, migration, and optimization across multiple geographical locations, while ensuring performance and cost-effectiveness.
Innovation Solution
Implementing a system that allows for the provisioning of virtual compute instances with virtual GPUs, enabling dynamic scaling and migration based on workload demands, and optimizing resource placement to minimize latency and costs through the selection of appropriate virtual GPU classes and physical resources within a provider network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtualization technologies are used to share physical computing devices among multiple users, then resource utilization efficiency is improved, but system complexity and management difficulty increase
Solution Approach 1:
The patent introduces a resource placement optimization system that acts as an intermediary between physical computing resources and virtual machines. This system automatically manages the complex mappings and allocations, reducing the burden on users while maintaining high resource utilization. The intermediary handles provisioning, monitoring, and optimization of virtualized resources across the distributed system.
2Productivity
If the scale and scope of distributed systems are increased, then computing capacity and service coverage are improved, but provisioning and management complexity increase
Solution Approach 1:
The patent implements dynamic resource placement optimization that automatically adapts to changing system conditions. The system continuously monitors resource utilization, workload demands, and system state, then dynamically adjusts virtual machine placements and resource allocations. This dynamic approach enables the distributed system to scale efficiently while automatically managing provisioning complexity through real-time optimization algorithms.
3Productivity
If virtual machines are migrated dynamically to different physical hosts, then load balancing and resource optimization are improved, but migration overhead and performance disruption increase
Solution Approach 1:
The patent employs preliminary action by pre-positioning virtual machines on physical hosts based on predicted workload patterns and resource requirements. The system performs advance optimization calculations and prepares migration plans before actual migrations are needed. This preliminary planning reduces the frequency and urgency of migrations, thereby minimizing migration overhead and performance disruption while maintaining effective load balancing.
4Adaptability or versatility
If multiple virtual GPU classes are provided to meet diverse graphics requirements, then adaptability to different applications is improved, but resource allocation complexity increases
Solution Approach 1:
The patent implements parameter changes by dynamically adjusting virtual GPU resource allocations based on application requirements and system state. The system modifies parameters such as memory allocation, processing power, and resource prioritization to match the specific needs of different applications. This parameter-based approach enables the system to provide multiple virtual GPU classes with varying capabilities while using automated algorithms to manage the complexity of resource allocation across diverse workloads.
Data Source
AI summary
Methods, systems, and computer-readable media for placement optimization for virtualized graphics processing are disclosed. A provider network comprises a plurality of instance locations for physical compute instances and a plurality of graphics processing unit (GPU) locations for physical GPUs. A GPU location for a physical GPU or an instance location for a physical compute instance is selected in the provider network. The GPU location or instance location is selected based at least in part on one or more placement criteria. A virtual compute instance with attached virtual GPU is provisioned. The virtual compute instance is implemented using the physical compute instance in the instance location, and the virtual GPU is implemented using the physical GPU in the GPU location. The physical GPU is accessible to the physical compute instance over a network. An application is executed using the virtual GPU on the virtual compute instance.