GPU Resource Scheduling With Predictive Allocation and Time Slicing
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Solution Overview
Problem
Kubernetes has limited support for GPU resources, static resource allocation, lacks fine-grained control, inefficient scheduling, and lacks holistic understanding of workloads and resources, particularly for AI/ML workloads with dynamic needs.
Innovation Solution
A machine learning-based platform that integrates with Kubernetes, GPU pooling, spatial and time slicing, and NVIDIA CUDA extensions to dynamically manage GPU resources based on workload prediction, using predictive scheduling and spatial/time slicing to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Kubernetes uses static resource allocation for GPUs, then resource allocation is simple and predictable, but GPU utilization efficiency deteriorates due to inability to adapt to dynamic workload needs
Solution Approach 1:
The patent implements dynamic GPU resource allocation by introducing a GPU allocation manager that continuously monitors workload characteristics and adjusts resource allocation in real-time. The system transitions from static pod-spec-based allocation to dynamic allocation based on actual GPU utilization patterns, workload type, and resource availability, thereby improving GPU utilization efficiency while adapting to changing conditions
Solution Approach 2:
The system employs feedback mechanisms through GPU monitoring agents that collect real-time data on GPU utilization, memory usage, and workload performance. This feedback is fed back to the allocation manager, which uses it to continuously optimize resource allocation decisions, creating a closed-loop control system that adapts to dynamic workload requirements
2Measurement precision
If Kubernetes provides fine-grained control over GPU resources, then resource allocation precision is improved, but system complexity increases due to need for workload behavior analysis
Solution Approach 1:
The patent segments the GPU resource management system into distinct functional components: workload characterization modules that analyze specific workload types, resource requirement estimators that calculate precise GPU needs, and allocation policies tailored to different workload categories. This segmentation enables fine-grained control by treating different workload types (e.g., training vs. inference, different frameworks) with specialized analysis and allocation strategies
Solution Approach 2:
The system changes allocation parameters dynamically based on workload characteristics. Instead of fixed allocation, it adjusts GPU memory allocation, compute resource allocation, and time slot assignments based on measured workload behavior patterns, achieving precision through parameter adaptation rather than complex static configurations
3Measurement precision
If Kubernetes scheduler has visibility into specific application workload requirements, then scheduling accuracy is improved, but information requirements increase
Solution Approach 1:
The patent introduces intermediary components between the scheduler and applications: workload characterization agents that collect and analyze application behavior, and resource requirement estimators that translate application needs into GPU resource specifications. These intermediaries process and summarize workload information, providing the scheduler with actionable insights without requiring direct access to detailed application internals
Solution Approach 2:
The system performs preliminary workload analysis and characterization before scheduling decisions are made. By pre-analyzing workload types, historical behavior patterns, and resource requirements, the system prepares scheduling recommendations in advance, enabling accurate scheduling without requiring the scheduler to process all workload details at decision time
4Productivity
If Kubernetes manages GPU resources with holistic understanding of workloads and platform, then resource allocation optimality is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal GPU resource management platform that handles multiple workload types (training, inference, different frameworks), various GPU models, and diverse allocation scenarios through a unified architecture. The allocation manager and characterization agents serve multiple functions across different contexts, achieving holistic management through multi-functionality rather than separate specialized systems for each scenario
Data Source
AI summary
The present invention provides a method for optimizing graphics-processing unit (GPU) utilization and a system thereof. The method includes the following steps: receiving an application workload which is to be executed on the GPU; predicting GPU resource requirements for the application workload; scheduling the application workload according to the prediction of the GPU resource requirements; dynamically allocating and deallocating GPU resources based on the prediction of the GPU resource requirements for the application workload; and executing the application workload on the GPU.