DocumentCode
55815
Title
A Tree Regression-Based Approach for VM Power Metering
Author
Chonglin Gu ; Pengzhou Shi ; Shuai Shi ; Hejiao Huang ; Xiaohua Jia
Author_Institution
Dept. of Comput. Sci. & Technol., Harbin Inst. of Technol., Shenzhen, China
Volume
3
fYear
2015
fDate
2015
Firstpage
610
Lastpage
621
Abstract
Cloud computing is developing so fast that more and more data centers have been built every year. This naturally leads to high-power consumption. Virtual machine (VM) consolidation is the most popular solution based on resource utilization. In fact, much more power can be saved if we know the power consumption of each VM. Therefore, it is significant to measure the power consumption of each VM for green cloud data centers. Since there is no device that can directly measure the power consumption of each VM, modeling methods have been proposed. However, current models are not accurate enough when multi-VMs are competing for resources on the same server. One of the main reasons is that the resource features for modeling are correlated with each other, such as CPU and cache. In this paper, we propose a tree regression-based method to accurately measure the power consumption of VMs on the same host. The merits of this method are that the tree structure will split the data set into partitions, and each is an easy-modeling subset. Experiments show that the average accuracy of our method is about 98% for different types of applications running in VMs.
Keywords
cloud computing; computer centres; energy consumption; green computing; power aware computing; power measurement; regression analysis; trees (mathematics); virtual machines; VM power metering; green cloud data centers; tree regression-based approach; virtual machine consolidation; Cloud computing; Data models; Power demand; Power measurement; Regression tree analysis; Tree regression; Virtual machining; VM; Virtual machine (VM); cloud computing; measure; metering; power; virtual machine;
fLanguage
English
Journal_Title
Access, IEEE
Publisher
ieee
ISSN
2169-3536
Type
jour
DOI
10.1109/ACCESS.2015.2430276
Filename
7102991
Link To Document