GPU Resource Pooling Across Clusters for Priority-Based Allocation
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Solution Overview
Problem
Conventional GPU resource management systems in data centers suffer from static allocation models, lack of multi-cluster federation capabilities, and monolithic resource management, leading to inefficiencies such as idle resources, underutilization, and manual configuration requirements, which hinder the effective utilization of GPUs across diverse workloads.
Innovation Solution
A dynamic resource allocation system that establishes a pool of computing resources across clusters, utilizing GPR prioritization policies like max-min fairness and automated provisioning to efficiently allocate GPUs to workspaces, enabling seamless cross-cluster management and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static resource allocation models are used, then configuration simplicity is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation by replacing static allocation models with a system that continuously monitors GPU usage patterns and automatically adjusts resource assignments. The controller receives usage data, analyzes it to identify optimization opportunities, and reallocates GPU resources dynamically to improve utilization efficiency while maintaining operational simplicity through automation.
2Device complexity
If monolithic resource management approaches are used, then system simplicity is maintained, but adaptability to diverse workloads deteriorates
Solution Approach 1:
The patent segments the monolithic resource management system into specialized components: a controller that handles high-level orchestration, workload managers that handle specific workload types (AI/ML vs. non-AI), and GPU nodes that execute tasks. This segmentation enables the system to adapt to diverse workloads through specialized management logic while keeping the overall architecture organized and manageable through clear separation of responsibilities.
Solution Approach 2:
The patent applies local quality by implementing workload-specific management strategies within the unified system. Different workload types (AI/ML workloads requiring high GPU memory bandwidth vs. non-AI workloads) receive tailored management approaches optimized for their specific requirements, allowing each local component to be optimized for its function while contributing to the overall system's versatility.
3Reliability
If manual configuration is required for advanced features, then system control is maintained, but operational efficiency deteriorates
Solution Approach 1:
The patent implements self-service through automated resource allocation and optimization. The controller automatically analyzes GPU usage patterns, identifies optimization opportunities, and executes reallocation decisions without requiring manual configuration. Advanced features like priority-based allocation, fairness policies, and workload-aware scheduling are performed automatically, maintaining system control through programmable policies while dramatically improving operational efficiency.
Solution Approach 2:
The patent employs feedback mechanisms where the controller continuously receives GPU usage data from workloads, analyzes this data to identify optimization opportunities, and adjusts resource allocations accordingly. This closed-loop feedback system maintains reliable control through programmable policies while automatically optimizing resource distribution based on real-time conditions, eliminating the need for manual reconfiguration.
4Device complexity
If cluster-centric designs without multi-cluster federation are used, then intra-cluster resource management simplicity is maintained, but cross-cluster resource sharing capability deteriorates
Solution Approach 1:
The patent implements universality by designing a controller that can manage both single-cluster and multi-cluster environments through a unified architecture. The controller is capable of orchestrating resource allocation across multiple clusters while maintaining the simplicity of single-cluster management when needed. This multi-functional design enables cross-cluster resource sharing and federation capabilities without requiring separate management systems, allowing the same controller to handle diverse deployment scenarios.
Data Source
AI summary
A technique manages computing resources on a container orchestration platform. Such a technique involves establishing a pool of computing resources on the container orchestration platform. Such a technique further involves, after the pool of computing resources is established, receiving graphics processing unit (GPU) provisioning requests (GPRs) which identify workspaces. Such a technique further involves allocating computing resources from the pool to the workspaces identified by the GPRs based on a set of GPR prioritization policies.