Virtual GPU Capacity Reservation via Segmented Availability
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Solution Overview
Problem
In large-scale distributed systems, managing concurrent requests for virtual graphics processing units (GPUs) leads to contention and inefficiency due to the lack of effective resource reservation mechanisms, particularly in multi-tenant environments where multiple clients require varying graphics processing capabilities and locations.
Innovation Solution
Implementing a capacity reservation system that segments data store availability information by constraints such as virtual GPU type and location, allowing for random selection within segments and pages to reduce contention, ensuring successful reservation of virtual GPUs for virtual compute instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized resource reservation system is used for virtual GPUs, then resource allocation control is improved, but contention and inefficiency increase due to concurrent requests from multiple clients
Solution Approach 1:
The patent segments the virtual GPU resource pool into multiple discrete resource units, each with specific configurations (GPU type, memory, compute capacity). This segmentation allows the reservation system to allocate specific units to different clients rather than managing a monolithic resource pool, reducing contention by enabling parallel allocation decisions for multiple clients simultaneously.
Solution Approach 2:
The patent implements preliminary capacity verification and reservation mechanisms where clients can check availability and reserve virtual GPU resources before actually needing them. The system performs capacity verification in advance, maintains reservation states, and allows clients to secure resources ahead of time, eliminating the need for all concurrent requests to compete for immediately available resources.
2Productivity
If virtualization technologies are used to share physical computing devices among multiple users, then resource utilization is improved, but managing concurrent requests leads to contention and inefficiency
Solution Approach 1:
The patent applies local quality by allowing different clients to access virtual GPU resources with different local characteristics and constraints. Each client's reservation request can specify local requirements (GPU type, location, capacity), and the system satisfies each request with resources matching those local qualities rather than providing uniform access to all resources.
Solution Approach 2:
The patent introduces an intermediary reservation system that sits between the physical GPU resources and multiple virtualized clients. This intermediary layer manages capacity verification, maintains availability information, and coordinates allocations, simplifying the management of concurrent requests by providing a centralized coordination point rather than allowing direct client-to-resource contention.
3Measurement precision
If capacity verification is performed for each concurrent request, then resource allocation accuracy is improved, but processing time and contention increase
Solution Approach 1:
The patent merges multiple capacity verification operations by maintaining a centralized availability information structure that tracks the state of all virtual GPU resources. Instead of performing independent verification for each request, the system updates and queries this merged state, allowing multiple requests to be processed against a unified view of resource availability, reducing redundant verification work.
Solution Approach 2:
The patent performs preliminary capacity verification by maintaining up-to-date availability information in advance of actual allocation requests. The system proactively tracks resource states, updates availability as resources are allocated or released, and makes this information readily accessible, so that when concurrent requests arrive, the verification step is minimized to a simple query rather than a full capacity analysis.
Data Source
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AI summary
Methods, systems, and computer-readable media for capacity reservation for virtualized graphics processing are disclosed. A request is received to attach a virtual GPU to a virtual compute instance. The request comprises one or more constraints. Availability information is retrieved from a data store that indicates virtual GPUs available in a provider network and matching the one or more constraints. A virtual GPU is selected from among the available virtual GPUs in the availability information. The selected virtual GPU is reserved for attachment to the virtual compute instance. The virtual compute instance is implemented using CPU resources and memory resources of a physical compute instance, the virtual GPU is implemented using a physical GPU in the provider network, and the physical GPU is accessible to the physical compute instance over a network.