Reinforcement Learning GPU Resource Management
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Solution Overview
Problem
Existing GPU resource management technologies in cloud environments struggle to efficiently allocate resources while ensuring Service Level Objectives (SLOs) are met, leading to potential performance degradation and increased energy consumption.
Innovation Solution
A method utilizing reinforcement learning to derive an optimal Multi-Instance GPU (MIG) instance configuration that meets SLO conditions and request rates, minimizing the allocation of GPU resources while maximizing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPU resources are allocated to meet SLO conditions and request rates, then service quality is improved, but energy consumption increases
Solution Approach 1:
The patent changes the parameter of MIG instance configuration (number of instances, memory allocation, computing resources) based on workload characteristics and SLO requirements. By dynamically adjusting these parameters using reinforcement learning, the system allocates the minimum necessary GPU resources to meet SLO conditions, thereby reducing energy consumption while maintaining service quality.
Solution Approach 2:
The patent implements dynamic resource allocation by using reinforcement learning to adapt MIG instance configurations in real-time based on workload patterns. The system continuously monitors workload characteristics and adjusts the number and configuration of MIG instances dynamically, allowing the GPU resources to be optimized for each specific workload rather than using static allocation.
2Reliability
If GPU resources are allocated to meet SLO conditions and request rates, then service quality is improved, but GPU resource utilization efficiency decreases
Solution Approach 1:
The patent optimizes MIG instance configuration parameters (number of instances, memory size, computing resources) based on workload characteristics. By using reinforcement learning to derive optimal configurations, the system ensures that GPU resources are allocated efficiently - neither over-provisioned nor under-provisioned - thereby maximizing resource utilization efficiency while meeting SLO requirements.
Solution Approach 2:
The patent divides a single GPU into multiple MIG instances with different configurations tailored to specific workload requirements. This segmentation allows different workloads to run in isolated instances with optimized resource allocations, improving overall GPU utilization efficiency by matching resource capacity to actual workload needs rather than using a one-size-fits-all approach.
3Reliability
If MIG instances are allocated according to preset partitions, then resource isolation is improved, but adaptability to different workload patterns decreases
Solution Approach 1:
The patent makes the MIG instance allocation dynamic by using reinforcement learning to adapt configurations based on workload patterns. Instead of using fixed preset partitions, the system learns optimal MIG instance configurations for different workload types and adjusts allocations in real-time. This maintains resource isolation benefits while providing adaptability to diverse workload requirements.
Solution Approach 2:
The patent changes the allocation parameters of MIG instances based on workload characteristics. The reinforcement learning model derives optimal configurations including the number of instances, memory allocation, and computing resources tailored to each workload's specific needs. This allows the system to maintain resource isolation through MIG's inherent separation while adapting resource distribution to match different workload patterns.
Data Source
AI summary
Disclosed herein are a method for GPU resource management using reinforcement learning and an apparatus for the same. The method performed by the apparatus includes deriving a Multi-Instance GPU (MIG) instance configuration that meets a Service Level Objective (SLO) condition and a request rate assigned to a workload by utilizing a pretrained reinforcement learning model and reorganizing MIG resources of a GPU device to correspond to the workload by transferring the MIG instance configuration to the GPU device.