Probabilistic Model-Based Analysis to Improve Software Energy Efficiency
Authors: 
Danilo da Silva Alves, Oseias Ayres Ferreira, Lucio Mauro Duarte, Paulo Henrique MaiaAuthors Info & Claims
Pages 132 - 136
Abstract
Software energy consumption has recently become a concern in software development. However, developers still lack knowledge about how to produce, evaluate and evolve their software considering energy consumption, which might limit their execution in some platforms and prevent users from adopting them. Towards providing more support for energy consumption analysis, we provide a set of properties to analyse software consumption considering not only energy costs but also probabilistic information. We demonstrate how to use, combine and interpret the results of analyses of these properties. We discuss experiments involving the analysis of the proposed properties in different scenarios and how, based on the results of these analyses, recommendations of possible actions to adjust energy consumption can be proposed.
References
[1]
S. Albers. 2010. Energy-efficient Algorithms. CACM 53, 5 (2010), 86--96.
[2]
A. Baqais et al. 2013. Bank Conflict-Free Access for CUDA-Based Matrix Transpose Algorithm on GPUs. In ICCAT. 160.
[3]
C. Baier et al. 2014. Probabilistic Model Checking for Energy-Utility Analysis. Springer International Publishing, Cham, 96--123.
[4]
C. Dubslaff et al. 2014. Probabilistic Model Checking for Energy Analysis in Software Product Lines. In MODULARITY '14 (Lugano, Switzerland). ACM, New York, NY, USA, 169--180.
[5]
D.M. Barbosa et al. 2017. Lotus@Runtime: A Tool for Runtime Monitoring and Verification of Self-adaptive Systems. In SEAMS '17 (Buenos Aires, Argentina) (SEAMS '17). IEEE Press, Piscataway, NJ, USA, 24--30.
[6]
J.L. Jodra et al. 2015. Efficient 3D transpositions in graphics processing units. Intl Journal of Parallel Programming 43, 5 (2015), 876--891.
[7]
J. Singh et al. 2015. Impact of developer choices on energy consumption of software on servers. Procedia Computer Science 62 (2015), 385--394.
[8]
K. Liu et al. 2015. Data-Oriented Characterization of Application-Level Energy Optimization. In FASE 2015, Alexander Egyed and Ina Schaefer (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 316--331.
[9]
L.M. Duarte et al. 2017. Using contexts to extract models from code. Software and Systems Modeling 16, 2 (2017), 523--557.
[10]
L.M. Duarte et al. 2018. Extraction of probabilistic behaviour models based on contexts. In MiSE '18. ACM Press, New York, New York, USA, 25--32.
[11]
L.M. Duarte et al. 2019. A Model-based Framework for the Analysis of Software Energy Consumption. In SBES 2019. ACM, 67--72.
[12]
M. Couto et al. 2014. Detecting anomalous energy consumption in android applications. In SBLP. Springer, 77--91.
[13]
M. Kwiatkowska et al. 2001. PRISM: Probabilistic Symbolic Model Checker. In Joint Tool Session of PNPM, MMB and PAPM/ProbMiv. 7--12.
[14]
R. Jayaseelan et al. 2006. Estimating the worst-case energy consumption of embedded software. In RTAS'06. IEEE, 81--90.
[15]
R. Pereira et al. 2016. The influence of the Java collection framework on overall energy consumption. In Proc. of the 5th GREENS. ACM, 15--21.
[16]
T. Feo et al. 1995. Greedy randomized adaptive search procedures. Journal of Global Optimization 6, 2 (1995), 109--133.
[17]
V. K. Singh et al. 2013. Estimating the Energy Consumption of Executing Software Processes. In GreenCom-iThings-CPSCom 2013. 94--101.
[18]
W. Oliveira et al. 2019. Recommending energy-efficient Java collections. In MSR 2019. IEEE Press, 160--170.
[19]
R.M. Keller. 1976. Formal Verification of Parallel Programs. CACM 19, 7 (July 1976), 371--384.
[20]
R. Pereira. 2017. Locating Energy Hotspots in Source Code. In ICSE 2017. 88--90.
[21]
G. Pinto and F. Castor. 2017. Energy Efficiency: A New Concern for Application Software Developers. CACM 60, 12 (December 2017), 68--75.
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Information
Published In
October 2020
901 pages
ISBN:9781450387538
DOI:10.1145/3422392
- General Chairs:
- Everton Cavalcante,
- Francisco Dantas,
- Thais Batista
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Published: 21 December 2020
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- Conselho Nacional de Desenvolvimento Científico e Tecnológico
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SBES '20
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