An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data

Dou, Wanchun, Xu, Xiaolong, Meng, Shunmei, Zhang, Xuyun, Hu, Chunhua, Yu, Shui and Yang, Jian 2016, An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data, Concurrency and computation: practice and experience, In Press, pp. 1-20, doi: 10.1002/cpe.3909.

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Title An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data
Author(s) Dou, Wanchun
Xu, Xiaolong
Meng, Shunmei
Zhang, Xuyun
Hu, Chunhua
Yu, ShuiORCID iD for Yu, Shui
Yang, Jian
Journal name Concurrency and computation: practice and experience
Season In Press
Start page 1
End page 20
Total pages 20
Publisher John Wiley & Sons
Place of publication Chichester, Eng.
Publication date 2016-07-13
ISSN 1532-0626
Keyword(s) energy-aware VM scheduling method
QoS enhancement
execution time
Summary Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on-demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first-class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy-aware manner. In this paper, we propose an energy-aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method.
Language eng
DOI 10.1002/cpe.3909
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, John Wiley & Sons
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Document type: Journal Article
Collection: School of Information Technology
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