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An effective energy testing framework for cloud workflow activities

Zhao, Zhou, Liu, Xiao, Li, Juan, Zhang, Kepi and Liu, Jin 2016, An effective energy testing framework for cloud workflow activities. In Cao, Jian, Liu, Xiao and Ren, Kaijun (ed), Process-aware systems, Springer, Berlin, Germany, pp.89-105, doi: 10.1007/978-981-10-1019-4_8.

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Title An effective energy testing framework for cloud workflow activities
Author(s) Zhao, Zhou
Liu, XiaoORCID iD for Liu, Xiao
Li, Juan
Zhang, Kepi
Liu, Jin
Title of book Process-aware systems
Editor(s) Cao, Jian
Liu, XiaoORCID iD for Liu, Xiao
Ren, Kaijun
Publication date 2016
Series Communications in computer and information science
Chapter number 8
Total chapters 11
Start page 89
End page 105
Total pages 16
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) cloud computing
energy testing
business workflow
time-series model
Summary Cloud computing as the latest computing paradigm has shown its promising future in business workflow systems facing massive concurrent user requests and complicated computing tasks. With the fast growth of cloud data centers, energy management especially energy monitoring and saving in cloud workflow systems has been attracting increasing attention. It is obvious that the energy for running a cloud workflow instance is mainly dependent on the energy for executing its workflow activities. However, existing energy management strategies mainly monitor the virtual machines instead of the workflow activities running on them, and hence it is difficult to directly monitor and optimize the energy consumption of cloud workflows. To address such an issue, in this paper, we propose an effective energy testing framework for cloud workflow activities. This framework can help to accurately test and analyze the baseline energy of physical and virtual machines in the cloud environment, and then obtain the energy consumption data of cloud workflow activities. Based on these data, we can further produce the energy consumption model and apply energy prediction strategies. Our experiments are conducted in an OpenStack based cloud computing environment. The effectiveness of our framework has been successfully verified through a detailed case study and a set of energy modelling and prediction experiments based on representative time-series models.
ISBN 9789811010187
ISSN 1865-0929
Language eng
DOI 10.1007/978-981-10-1019-4_8
Field of Research 080599 Distributed Computing not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1.1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2016, Springer Science + Business Media
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Document type: Book Chapter
Collection: School of Information Technology
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