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An energy-aware QoS enhanced method for service computing across clouds and data centers

Dou, Wanchun, Xu, Xiaolong, Meng, Shunmei and Yu, Shui 2015, An energy-aware QoS enhanced method for service computing across clouds and data centers, in CBD 2015: Proceedings of the Advanced Cloud and Big Data 2015 International Conference, IEEE, Piscataway, N.J., pp. 80-87, doi: 10.1109/CBD.2015.23.

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Title An energy-aware QoS enhanced method for service computing across clouds and data centers
Author(s) Dou, Wanchun
Xu, Xiaolong
Meng, Shunmei
Yu, Shui
Conference name Advanced Cloud and Big Data. International Conference (3rd : 2015 : Yangzhou, China)
Conference location Yangzhou, China
Conference dates 30 Oct. - 1 Nov. 2015
Title of proceedings CBD 2015: Proceedings of the Advanced Cloud and Big Data 2015 International Conference
Editor(s) [Unknown]
Publication date 2015
Conference series Advanced Cloud and Big Data International Conference
Start page 80
End page 87
Total pages 9
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) energy-aware QoS enhanced method
service computing
price
execution time
Summary QoS plays a key role in evaluating a service or a service composition plan across clouds and data centers. Currently, the energy cost of a service's execution is not covered by the QoS framework, and a service's price is often fixed during its execution. However, energy consumption has a great contribution in determining the price of a cloud service. As a result, it is not reasonable if the price of a cloud service is calculated with a fixed energy consumption value, if part of a service's energy consumption could be saved during its execution. Taking advantage of the dynamic energy-Aware optimal technique, a QoS enhanced method for service computing is proposed, in this paper, through virtual machine (VM) scheduling. Technically, two typical QoS metrics, i.e., the price and the execution time are taken into consideration in our method. Moreover, our method consists of two dynamic optimal phases. The first optimal phase aims at dynamically benefiting a user with discount price by transparently migrating his or her task execution from a VM located at a server with high energy consumption to a low one. The second optimal phase aims at shortening task's execution time, through transparently migrating a task execution from a VM to another one located at a server with higher performance. Experimental evaluation upon large scale service computing across clouds demonstrates the validity of our method.
ISBN 9781467385374
Language eng
DOI 10.1109/CBD.2015.23
Field of Research 080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084561

Document type: Conference Paper
Collection: School of Information Technology
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