Products go green: worst-case energy consumption in software product lines
| Main Author: | |
|---|---|
| Publication Date: | 2017 |
| Other Authors: | , , , , |
| Language: | eng |
| Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Download full: | http://hdl.handle.net/1822/65361 |
Summary: | The optimization of software to be (more) energy efficient is becoming a major concern for the software industry. Although several techniques have been presented to measure energy consumption for software, none has addressed software product lines (SPLs). Thus, to measure energy consumption of a SPL, the products must be generated and measured individually, which is too costly.In this paper, we present a technique and a prototype tool to statically estimate the worst case energy consumption for SPL. The goal is to provide developers with techniques and tools to reason about the energy consumption of all products in a SPL, without having to produce, run and measure the energy in all of them.Our technique combines static program analysis techniques and worst case execution time prediction with energy consumption analysis. This technique analyzes all products in a feature-sensitive manner, that is, a feature used in several products is analyzed only once, while the energy consumption is estimated once per product.We implemented our technique in a tool called Serapis. We did a preliminary evaluation using a product line for image processing implemented in C. Our experiments considered 7 products from such line and our initial results show that the tool was able to estimate the worst-case energy consumption with a mean error percentage of 9.4% and standard deviation of 6.2% when compared with the energy measured when running the products. |
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Products go green: worst-case energy consumption in software product linesCiências Naturais::Ciências da Computação e da InformaçãoScience & TechnologyThe optimization of software to be (more) energy efficient is becoming a major concern for the software industry. Although several techniques have been presented to measure energy consumption for software, none has addressed software product lines (SPLs). Thus, to measure energy consumption of a SPL, the products must be generated and measured individually, which is too costly.In this paper, we present a technique and a prototype tool to statically estimate the worst case energy consumption for SPL. The goal is to provide developers with techniques and tools to reason about the energy consumption of all products in a SPL, without having to produce, run and measure the energy in all of them.Our technique combines static program analysis techniques and worst case execution time prediction with energy consumption analysis. This technique analyzes all products in a feature-sensitive manner, that is, a feature used in several products is analyzed only once, while the energy consumption is estimated once per product.We implemented our technique in a tool called Serapis. We did a preliminary evaluation using a product line for image processing implemented in C. Our experiments considered 7 products from such line and our initial results show that the tool was able to estimate the worst-case energy consumption with a mean error percentage of 9.4% and standard deviation of 6.2% when compared with the energy measured when running the products.We would like to thank Simao Melo de Sousa (University of Beira Interior) for helpful discussions about the topics of this paper, and to the anonymous reviewers for the valuable comments and feedback. This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundacao para a Ciencia e a Tecnologia within project POCI-01-0145-FEDER-016718, and FLAD/NSF under the project with ref. 278/2016.Association for Computing Machinery (ACM)Universidade do MinhoCouto, Marco Domingos MendesBorba, PauloCunha, Jácome Miguel CostaFernandes, João Paulo SoaresPereira, RuiSaraiva, João20172017-01-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/65361engMarco Couto, Paulo Borba, Jácome Cunha, João Paulo Fernandes, Rui Pereira,and João Saraiva. 2017. Products go Green: Worst-Case Energy Consump-tion in Software Product Lines. InProceedings of SPLC ’17, Sevilla, Spain,September 25-29, 2017,10 pages.DOI: 10.1145/3106195.3106214978145035221510.1145/3106195.3106214info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T06:18:37Zoai:repositorium.sdum.uminho.pt:1822/65361Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T15:48:42.690230Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
| dc.title.none.fl_str_mv |
Products go green: worst-case energy consumption in software product lines |
| title |
Products go green: worst-case energy consumption in software product lines |
| spellingShingle |
Products go green: worst-case energy consumption in software product lines Couto, Marco Domingos Mendes Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
| title_short |
Products go green: worst-case energy consumption in software product lines |
| title_full |
Products go green: worst-case energy consumption in software product lines |
| title_fullStr |
Products go green: worst-case energy consumption in software product lines |
| title_full_unstemmed |
Products go green: worst-case energy consumption in software product lines |
| title_sort |
Products go green: worst-case energy consumption in software product lines |
| author |
Couto, Marco Domingos Mendes |
| author_facet |
Couto, Marco Domingos Mendes Borba, Paulo Cunha, Jácome Miguel Costa Fernandes, João Paulo Soares Pereira, Rui Saraiva, João |
| author_role |
author |
| author2 |
Borba, Paulo Cunha, Jácome Miguel Costa Fernandes, João Paulo Soares Pereira, Rui Saraiva, João |
| author2_role |
author author author author author |
| dc.contributor.none.fl_str_mv |
Universidade do Minho |
| dc.contributor.author.fl_str_mv |
Couto, Marco Domingos Mendes Borba, Paulo Cunha, Jácome Miguel Costa Fernandes, João Paulo Soares Pereira, Rui Saraiva, João |
| dc.subject.por.fl_str_mv |
Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
| topic |
Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
| description |
The optimization of software to be (more) energy efficient is becoming a major concern for the software industry. Although several techniques have been presented to measure energy consumption for software, none has addressed software product lines (SPLs). Thus, to measure energy consumption of a SPL, the products must be generated and measured individually, which is too costly.In this paper, we present a technique and a prototype tool to statically estimate the worst case energy consumption for SPL. The goal is to provide developers with techniques and tools to reason about the energy consumption of all products in a SPL, without having to produce, run and measure the energy in all of them.Our technique combines static program analysis techniques and worst case execution time prediction with energy consumption analysis. This technique analyzes all products in a feature-sensitive manner, that is, a feature used in several products is analyzed only once, while the energy consumption is estimated once per product.We implemented our technique in a tool called Serapis. We did a preliminary evaluation using a product line for image processing implemented in C. Our experiments considered 7 products from such line and our initial results show that the tool was able to estimate the worst-case energy consumption with a mean error percentage of 9.4% and standard deviation of 6.2% when compared with the energy measured when running the products. |
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2017 |
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2017 2017-01-01T00:00:00Z |
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conference paper |
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info:eu-repo/semantics/publishedVersion |
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publishedVersion |
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http://hdl.handle.net/1822/65361 |
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http://hdl.handle.net/1822/65361 |
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eng |
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eng |
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Marco Couto, Paulo Borba, Jácome Cunha, João Paulo Fernandes, Rui Pereira,and João Saraiva. 2017. Products go Green: Worst-Case Energy Consump-tion in Software Product Lines. InProceedings of SPLC ’17, Sevilla, Spain,September 25-29, 2017,10 pages.DOI: 10.1145/3106195.3106214 9781450352215 10.1145/3106195.3106214 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Association for Computing Machinery (ACM) |
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Association for Computing Machinery (ACM) |
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