GPU Resource Scaling via Message Bus Prioritization
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Solution Overview
Problem
Current cloud computing service technologies lack the ability to auto-scale GPU resources based on utilization and queued requests, leading to inefficient GPU usage, as GPU processing is typically manually set and lacks prioritization for different services, resulting in sequential execution of requests.
Innovation Solution
A method and apparatus for dynamically scaling GPU resources in a cloud computing system by receiving requests from client devices, queuing them based on a preset prioritization scheme, and scaling GPU instances accordingly, using a message bus with prioritization loops to manage high, medium, and low priority requests effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPU resources are manually set and dedicated instances are created for different services, then service reliability is improved, but device complexity and resource utilization efficiency worsen
Solution Approach 1:
The patent implements a unified GPU resource pool that serves multiple services simultaneously through virtualization and containerization technologies. Instead of creating separate dedicated GPU instances for each service, the system allows a single GPU resource pool to be dynamically shared and allocated to different services based on their needs, thereby reducing device complexity while maintaining service reliability through resource isolation and guaranteed allocation.
Solution Approach 2:
The patent introduces dynamic resource allocation mechanisms where GPU resources are not statically assigned but rather dynamically adjusted based on real-time service demands, queue depths, and priority levels. This dynamic approach allows the system to adapt resource distribution automatically, reducing the need for pre-configured dedicated instances while ensuring each service receives appropriate resources when needed.
2Device complexity
If GPU processing is manually configured without prioritization, then device complexity is reduced, but productivity and response time worsen due to sequential request execution
Solution Approach 1:
The patent segments the request processing system into multiple priority queues (high, medium, low priority) and implements corresponding processing threads. This segmentation allows different types of requests to be handled in parallel according to their priority levels, significantly improving productivity without adding complex manual configuration. The segmentation is automatically managed by the system based on request characteristics.
Solution Approach 2:
The patent implements an automated prioritization and scheduling system that self-manages request routing and GPU resource allocation without requiring manual intervention. The system automatically categorizes incoming requests into priority queues, monitors queue depths, and dynamically adjusts resource allocation based on current system state, thereby improving productivity while keeping device complexity low through automation.
3Adaptability or versatility
If separate GPU instances are created for different services, then service adaptability is improved, but loss of substance (resource waste) increases due to underutilization
Solution Approach 1:
The patent merges multiple service-specific GPU instances into a unified shared GPU resource pool. By combining resources that would otherwise sit underutilized in dedicated instances, the system achieves higher overall resource utilization. Services access the shared pool through virtualized interfaces that provide isolation and guaranteed minimum allocations, maintaining service adaptability while eliminating resource waste from idle dedicated instances.
Solution Approach 2:
The patent implements a resource recovery mechanism where GPU resources are dynamically released from low-priority or idle services and reallocated to high-priority services that need them. The system continuously monitors resource usage and automatically recovers unused capacity, making it available for other services without requiring permanent dedicated allocations, thus reducing resource waste while maintaining service adaptability.
Data Source
AI summary
A method and apparatus for scaling resources of a GPU in a cloud computing system are provided. The method includes receiving requests for services from a client device, queuing the received requests in a message bus based on a preset prioritization scheme; and scaling the resources of the GPU for the requests queued in the message bus according to a preset prioritization loop.