vGPU Placement Neural Network for Workload Optimization
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Solution Overview
Problem
Existing workload placement models in vGPU-enabled systems often result in sub-optimal placement of virtual machines, leading to unbalanced hosts, network saturation, and inefficient resource utilization due to their inefficiency across varying hardware configurations and workload arrival rates.
Innovation Solution
The implementation of a vGPU placement neural network that utilizes a composite efficiency metric to optimize the selection and placement of workloads on GPUs, combining multiple neural networks to maximize GPU utilization and minimize wait time through dynamic scheduling policies and vGPU profile management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional workload placement models are used, then implementation is simple, but resource utilization is inefficient
Solution Approach 1:
The patent replaces traditional mechanical/work-based placement models with a neural network-based intelligent system. The neural network learns optimal placement strategies from historical data and automatically makes placement decisions, substituting complex manual or rule-based mechanisms with an adaptive AI system that improves resource utilization without requiring manual intervention.
Solution Approach 2:
The patent changes the fundamental parameters of the placement model by introducing neural network weights, biases, and learning rates instead of fixed rules. The system dynamically adjusts placement decisions based on learned patterns from historical workload data, transforming static placement algorithms into adaptive parameter-driven models that optimize GPU utilization across varying workload conditions.
2Adaptability or versatility
If static placement models are used, then system is stable, but adaptability to varying hardware configurations is poor
Solution Approach 1:
The patent transforms the placement model from static to dynamic by implementing a neural network that continuously learns from historical workload data and adapts to changing hardware configurations. The system maintains stability through consistent learning patterns while adapting to new GPU types, quantities, and workload characteristics, allowing it to handle evolving datacenter environments without complete reconfiguration.
Solution Approach 2:
The neural network placement model performs self-learning and self-optimization by automatically analyzing historical workload data and adjusting its internal parameters without external intervention. This self-service capability enables the system to adapt to varying hardware configurations autonomously while maintaining operational stability through learned best practices.
3Productivity
If simple placement strategies are used, then implementation is easy, but network saturation occurs
Solution Approach 1:
The patent replaces simple mechanical placement strategies with an intelligent neural network system that analyzes historical workload patterns to predict and prevent network saturation. The neural network learns optimal placement decisions that distribute workloads across hosts in a way that balances network utilization, preventing saturation while maximizing GPU usage.
Solution Approach 2:
The system implements feedback mechanisms by continuously analyzing historical workload data and placement outcomes to improve future decisions. The neural network learns from past network utilization patterns and adjusts placement strategies to avoid saturation, creating a closed-loop system that optimizes network efficiency alongside GPU utilization.
4Productivity
If workload placement is optimized for one scenario, then performance is maximized for that scenario, but performance degrades in other scenarios
Solution Approach 1:
The patent creates a universal placement model through the neural network that can handle multiple scenarios and hardware configurations simultaneously. The system learns generalizable patterns from diverse historical data that apply across different GPU types, quantities, and workload characteristics, enabling it to maintain high placement efficiency whether the datacenter has 3 GPUs or 24 GPUs, or encounters various workload arrival rates.
Data Source
AI summary
Disclosed are aspects of workload selection and placement in systems that include graphics processing units (GPUs) that are virtual GPU (vGPU) enabled. In some aspects, workloads are assigned to virtual graphics processing unit (vGPU)-enabled graphics processing units (GPUs) based on a variety of vGPU placement models. A number of vGPU placement neural networks are trained to maximize a composite efficiency metric based on workload data and GPU data for the plurality of vGPU placement models. A combined neural network selector is generated using the vGPU placement neural networks, and utilized to assign a workload to a vGPU-enabled GPU.