Energy-Efficient GPU-Intensive Workload Scheduling for Data Centers
Author:
Affiliation:
1. California State University,Department of Computer Science,Northridge
2. Brown University,Department of Computer Science
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10459339/10459727/10459846.pdf?arnumber=10459846
Reference17 articles.
1. Recalibrating global data center energy-use estimates
2. Machine Learning based Thermal Prediction for Energy-efficient Cloud Computing
3. Energy-Efficient Thermal-Aware Task Scheduling for Homogeneous High-Performance Computing Data Centers: A Cyber-Physical Approach
4. An Energy-Efficient Management Mechanism for Large-Scale Server Clusters
5. An Energy-optimized Embedded load balancing using DVFS computing in Cloud Data centers
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Machine Learning-Based Energy-efficient Workload Management for Data Centers;2024 IEEE 21st Consumer Communications & Networking Conference (CCNC);2024-01-06
2. Deep-Hill: An Innovative Cloud Resource Optimization Algorithm by Predicting SaaS Instance Configuration Using Deep Learning;IEEE Access;2024
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