Workload-Processor Resource Scheduling for Noisy Neighbor Mitigation
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Solution Overview
Problem
Current GPU scheduling methods for information handling systems, particularly in VDI environments and AI/ML/DL operations, often result in suboptimal performance due to the 'noisy neighbor' issue, where high-resource workloads degrade the performance of other workloads, and administrators lack a systematic approach to allocate resources effectively.
Innovation Solution
An Information Handling System (IHS) with a workload/processor resource scheduling engine that monitors performance, identifies correlations between workload schedules and system operating parameters, and adjusts resource allocation to optimize the performance of subsequent workloads based on these correlations, allowing for targeted operating-parameter-based requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If GPU resources are shared among multiple workloads to improve resource utilization, then productivity increases, but noisy neighbor issues cause performance degradation for individual workloads
Solution Approach 1:
The system continuously monitors workload performance metrics and resource utilization levels, using this feedback to dynamically adjust scheduling decisions. The scheduling engine observes performance degradation patterns and modifies resource allocation in real-time to prevent noisy neighbor effects while maintaining high utilization.
Solution Approach 2:
The scheduling system transitions from static, administrator-configured schedules to dynamic, adaptive scheduling that automatically adjusts resource allocation based on current system conditions, workload characteristics, and performance feedback. This enables the system to optimize both utilization and performance adaptively.
2Reliability
If administrators manually configure GPU scheduling to address noisy neighbor issues, then workload performance can be optimized, but the complexity of operation increases
Solution Approach 1:
The scheduling system performs self-configuration by automatically analyzing workload patterns, resource utilization, and performance metrics to generate optimal scheduling policies without requiring administrator intervention. The system learns from observed patterns and autonomously adjusts schedules to eliminate noisy neighbor effects.
Solution Approach 2:
The system dynamically modifies scheduling parameters such as time slots, resource allocation ratios, and priority levels based on observed workload characteristics and performance feedback, eliminating the need for manual parameter tuning by administrators.
3Productivity
If GPU resources are allocated to high-resource workloads to meet their requirements, then those workloads perform well, but other workloads suffer from resource contention
Solution Approach 1:
The scheduling system implements periodic time-sliced allocation where high-resource workloads receive dedicated resource bursts at scheduled intervals, while other workloads receive resources during off-peak periods. This periodic sharing pattern ensures both high-resource and low-resource workloads achieve acceptable performance levels.
Solution Approach 2:
The system applies different scheduling strategies and resource allocation patterns to different workload types based on their specific requirements. High-resource workloads receive aggressive allocation during their active periods, while sensitive workloads receive protected, consistent resource guarantees, creating locally optimized quality of service for each workload category.
Data Source
AI summary
A workload/processor resource scheduling system is coupled to a processing system. The workload/processor resource scheduling system monitors a performance of first workload(s) by the processing system according to a workload/processor resource schedule, and identifies a correlation between the performance of the first workload(s) according to the workload/processor resource schedule, and an operating level of a processing system operating parameter for the processing system when performing the first workload(s) according to the workload/processor resource schedule. Based on the correlation, the workload/processor resource schedule and the processing system operating parameter are linked. Subsequently, an operating-parameter-based request is received to produce the operating level of the processing system operating parameter when performing a second workload and, based on the operating-parameter-based request and the linking of the workload/processor resource schedule and the processing system operating parameter, the second workload is performed by the processing system based on the workload processor resource schedule.