A software energy analysis method using ExecutableUML

Ryusuke Yoshimoto; Tomonori Kadono; Kenji Hisazumi; Akira Fukuda

doi:10.1109/TENCON.2016.7847993

A software energy analysis method using ExecutableUML

Ryusuke Yoshimoto, Tomonori Kadono, Kenji Hisazumi, Akira Fukuda

Advanced Software Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

The power consumption of an embedded system has been increased. It is important to analyze energy consumption while taking software behaviors into account to reduce energy consumption. Some studies propose model-based energy analysis. Although these method can estimate energy usage within a short time as compared with code-based analysis, the problem is that these technique cannot detect the bottleneck of energy consumption in software behavior in the design stage. This paper proposes an energy consumption analysis method for multi-granularity of ExecutableUML models. The method is useful for detecting the bottleneck of energy consumption in software behavior in the design stage. We also demonstrate that the error of our energy analysis method is, on average, 9.0%.

Original language	English
Title of host publication	Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	218-221
Number of pages	4
ISBN (Electronic)	9781509025961
DOIs	https://doi.org/10.1109/TENCON.2016.7847993
Publication status	Published - Feb 8 2017
Event	2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore Duration: Nov 22 2016 → Nov 25 2016

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Other

Other	2016 IEEE Region 10 Conference, TENCON 2016
Country/Territory	Singapore
City	Singapore
Period	11/22/16 → 11/25/16

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/TENCON.2016.7847993

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Yoshimoto, R., Kadono, T., Hisazumi, K., & Fukuda, A. (2017). A software energy analysis method using ExecutableUML. In Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016 (pp. 218-221). Article 7847993 (IEEE Region 10 Annual International Conference, Proceedings/TENCON). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2016.7847993

A software energy analysis method using ExecutableUML. / Yoshimoto, Ryusuke; Kadono, Tomonori; Hisazumi, Kenji et al.
Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 218-221 7847993 (IEEE Region 10 Annual International Conference, Proceedings/TENCON).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yoshimoto, R, Kadono, T, Hisazumi, K & Fukuda, A 2017, A software energy analysis method using ExecutableUML. in Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016., 7847993, IEEE Region 10 Annual International Conference, Proceedings/TENCON, Institute of Electrical and Electronics Engineers Inc., pp. 218-221, 2016 IEEE Region 10 Conference, TENCON 2016, Singapore, Singapore, 11/22/16. https://doi.org/10.1109/TENCON.2016.7847993

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AB - The power consumption of an embedded system has been increased. It is important to analyze energy consumption while taking software behaviors into account to reduce energy consumption. Some studies propose model-based energy analysis. Although these method can estimate energy usage within a short time as compared with code-based analysis, the problem is that these technique cannot detect the bottleneck of energy consumption in software behavior in the design stage. This paper proposes an energy consumption analysis method for multi-granularity of ExecutableUML models. The method is useful for detecting the bottleneck of energy consumption in software behavior in the design stage. We also demonstrate that the error of our energy analysis method is, on average, 9.0%.

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A software energy analysis method using ExecutableUML

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