Machine Learning-Based Energy-efficient Workload Management for Data Centers

Cooling costs count for a significant part of the total energy consumption in data centers, and previous researchers mainly focused on investigating thermal-ware workload distribution strategies for CPU-intensive workloads. This paper introduces a novel machine learning-based approach that aims at r...

Full description

Saved in:
Bibliographic Details
Main Authors: Smith, Matthew, Zhao, Luke, Cordova, Jonathan, Jiang, Xunfei, Ebrahimi, Mahdi
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Cooling
Costs
Data centers
Energy consumption
energy-efficient
machine learning
Machine learning algorithms
Scheduling algorithms
workload management
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cooling costs count for a significant part of the total energy consumption in data centers, and previous researchers mainly focused on investigating thermal-ware workload distribution strategies for CPU-intensive workloads. This paper introduces a novel machine learning-based approach that aims at reducing energy consumption through thermal-aware workload distribution to build energy-efficient data centers for GPU-intensive workload. To achieve this goal, the study employs the GPUCloudSim Plus simulator, which effectively models the distribution of GPU-intensive applications under diverse workloads and utilizations. The integration of advanced machine learning models allows for accurate temperature predictions and comprehensive evaluation of the proposed algorithm's performance. We evaluated our ThermalAwareGpu workload scheduling algorithm, and it saved up to 12.82% of computing cost compared to the baseline algorithms. Our future work will explore the estimation of data center cooling energy and conduct in-depth comparisons of different workload balancing algorithms on more intensive experiments.
ISSN:2331-9860
DOI:10.1109/CCNC51664.2024.10454842