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A resource scheduling method for cloud data centers based on thermal management

delete2023-06-10
delete7
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OA
AI
L
Li Mao
R
Rui Chen
H
Huiwen Cheng
林伟伟 cover
林伟伟 (Weiwei Lin) *
刘波 (Bo Liu) *
J
James Z. Wang
DOI:10.1186/s13677-023-00462-2delete
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Abstract

Abstract

En 中文
With the rapid growth of cloud computing services, the high energy consumption of cloud data centers has become a critical concern of the cloud computing society. While virtual machine (VM) consolidation is often used to reduce energy consumption, excessive VM consolidation may lead to local hot spots and increase the risk of equipment failure. One possible solution to this problem is to utilize thermal-aware scheduling, but existing approaches have trouble realizing the balance between SLA and energy consumption. This paper proposes a novel method to manage cloud data center resources based on thermal management (TM-VMC), which optimizes total energy consumption and proactively prevents hot spots from a global perspective. Its VM consolidation process includes four phases where the VMs scheduler uses an improved ant colony algorithm (UACO) to find appropriate target hosts for VMs based on server temperature and utilization status obtained in real-time. Experimental results show that the TM-VMC approach can proactively avoid data center hot spots and significantly reduce energy consumption while maintaining low Service Level Agreement (SLA) violation rates compared to existing mainstream VM consolidation algorithms with workloads from real-world data centers.
Keywords:
Cloud data center
Energy consumption
Thermal management
Virtual machine consolidation

Journal

J
Journal of Cloud Computing-Advances Systems and Applications
IF:
4.3
Papers:
738
Citations:
2.2K

Organization

S
south china normal university
Scholars:
2.0W
Papers: 1.3W
Citations: 13
G
Guangdong Police College
Scholars:
36
Papers: 29
Citations: 49
C
Clemson University
Scholars:
1.3W
Papers: 1.1W
Citations: 1.4W
S
south china university of technology
Scholars:
6.8W
Papers: 5.1W
Citations: 85
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