DocumentCode
1605814
Title
A resource scheduling algorithm of cloud computing based on energy efficient optimization methods
Author
Luo, Liang ; Wu, Wenjun ; Di, Dichen ; Zhang, Fei ; Yan, Yizhou ; Mao, Yaokuan
Author_Institution
Nat. Key Lab. of Software, Environ. Dev., Bei Hang Univ., Beijing, China
fYear
2012
Firstpage
1
Lastpage
6
Abstract
Cloud computing has been emerging as a flexible and powerful computational architecture to offer ubiquitous services to users. It accommodates interconnected hardware and software resources in a unified way, which is different to traditional computational environments. A variety of hardware and software resources are integrated together as a resource pool, the software is no longer resided in a single hardware environment, it is performed upon the schedule of the resource pool for optimized resource utilization. The optimization of energy consumption in the cloud computing environment is the question how to use various energy conservation strategies to efficiently allocate resources. In this paper, we study the relationship between infrastructure components and power consumption of the cloud computing environment, and discuss the matching of task types and component power adjustment methods, and then we present a resource scheduling algorithm of Cloud Computing based on energy efficient optimization methods. The experimental results demonstrate that, for jobs that not fully utilized the hardware environment, using our algorithm can significantly reduce energy consumption.
Keywords
cloud computing; energy conservation; resource allocation; cloud computing environment; component power adjustment methods; computational architecture; energy conservation strategies; energy consumption optimization; energy efficient optimization methods; hardware resources; infrastructure components; optimized resource utilization; power consumption; resource allocation; resource pool; resource scheduling algorithm; software resources; task types; ubiquitous services; Cloud computing; Energy consumption; Energy measurement; Hardware; Monitoring; Scheduling algorithms; Servers; Cloud Computing; Cluster; Energy Efficient;
fLanguage
English
Publisher
ieee
Conference_Titel
Green Computing Conference (IGCC), 2012 International
Conference_Location
San Jose, CA
Print_ISBN
978-1-4673-2155-6
Electronic_ISBN
978-1-4673-2153-2
Type
conf
DOI
10.1109/IGCC.2012.6322251
Filename
6322251
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