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Automated analysis of performance and energy consumption for cloud applications

Chen, Feifei, Grundy, John, Schneider, Jean-Guy, Yang, Yun and He, Qiang 2014, Automated analysis of performance and energy consumption for cloud applications, in ICPE 2014 : Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering, ACM Digital Library, [Dublin, Ireland], pp. 39-50, doi: 10.1145/2568088.2568093.

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Title Automated analysis of performance and energy consumption for cloud applications
Author(s) Chen, Feifei
Grundy, JohnORCID iD for Grundy, John orcid.org/0000-0003-4928-7076
Schneider, Jean-Guy
Yang, Yun
He, Qiang
Conference name Performance Engineering. Conference (5th : 2014 : Dublin, Ireland)
Conference location Dublin, Ireland
Conference dates 22-26 Mar. 2014
Title of proceedings ICPE 2014 : Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering
Editor(s) [Unknown]
Publication date 2014
Start page 39
End page 50
Total pages 12
Publisher ACM Digital Library
Place of publication [Dublin, Ireland]
Summary In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected finegrained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.
ISBN 9781450327336
Language eng
DOI 10.1145/2568088.2568093
Field of Research 080309 Software Engineering
Socio Economic Objective 890202 Application Tools and System Utilities
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, ACM Digital Library
Persistent URL http://hdl.handle.net/10536/DRO/DU:30081796

Document type: Conference Paper
Collection: School of Information Technology
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