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StressCloud: A Tool for Analysing Performance and Energy Consumption of Cloud Applications.

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Title: StressCloud: A Tool for Analysing Performance and Energy Consumption of Cloud Applications.
Authors: Feifei Chen1 feifeichen@swin.edu.au, John Grundy1 jgrundy@swin.edu.au, Schneider, Jean-Guy1 jschneider@swin.edu.au, Yun Yang1 yyang@swin.edu.au, Qiang He1 qhe@swin.edu.au
Source: ICSE: International Conference on Software Engineering. 2015, p721-724. 4p.
Subjects: Computer software research, Software engineering, Energy consumption, Cloud computing, Automation, Software engineers
Abstract: Finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance of cloud applications is an extremely challenging task. It requires the evaluation of system performance and energy consumption under a wide variety of realistic workloads and deployment configurations. This paper demonstrates StressCloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. StressCloud supports 1) the modelling of realistic cloud application workloads, 2) the automatic generation and running of load tests, and 3) the profiling of system performance and energy consumption. A demonstration video can be accessed at: https://www.youtube.com/watch?v=0l4_a_CNtVQ [ABSTRACT FROM AUTHOR]
Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="AR" term="%22Feifei+Chen%22">Feifei Chen</searchLink><relatesTo>1</relatesTo><i> feifeichen@swin.edu.au</i><br /><searchLink fieldCode="AR" term="%22John+Grundy%22">John Grundy</searchLink><relatesTo>1</relatesTo><i> jgrundy@swin.edu.au</i><br /><searchLink fieldCode="AR" term="%22Schneider%2C+Jean-Guy%22">Schneider, Jean-Guy</searchLink><relatesTo>1</relatesTo><i> jschneider@swin.edu.au</i><br /><searchLink fieldCode="AR" term="%22Yun+Yang%22">Yun Yang</searchLink><relatesTo>1</relatesTo><i> yyang@swin.edu.au</i><br /><searchLink fieldCode="AR" term="%22Qiang+He%22">Qiang He</searchLink><relatesTo>1</relatesTo><i> qhe@swin.edu.au</i>
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  Data: Finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance of cloud applications is an extremely challenging task. It requires the evaluation of system performance and energy consumption under a wide variety of realistic workloads and deployment configurations. This paper demonstrates StressCloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. StressCloud supports 1) the modelling of realistic cloud application workloads, 2) the automatic generation and running of load tests, and 3) the profiling of system performance and energy consumption. A demonstration video can be accessed at: https://www.youtube.com/watch?v=0l4_a_CNtVQ [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/ICSE.2015.232
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        Text: English
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        PageCount: 4
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      – SubjectFull: Energy consumption
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      – SubjectFull: Cloud computing
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      – SubjectFull: Automation
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      – SubjectFull: Software engineers
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              Text: 2015
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