Self-adaptation approaches for energy efficiency: A systematic literature review

Fahimeh Alizadeh Moghaddam, P. Lago, Iulia Cristina Ban

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

The increasing energy demands of software systems have set an essential software quality requirement: Energy efficiency. At the same time, the many contextual changes faced by software systems during execution can hamper their functionality and overall quality. To address both problems, self-adaptation approaches can empower software systems, at both design-time and runtime, to adapt to dynamic conditions. In this way, software systems can be more resilient to failure, hence more trustful to satisfy the demands of modern digital society. In this paper, we perform a systematic literature review to study the state-of-the-art on existing self-adaptation approaches for energy efficiency. We analyze the identified approaches from three different perspectives, namely publication trends, application domains, and types of software systems. Our findings can help solution providers to make guided decisions to enable self-adaptability in designing and engineering software systems.

Original languageEnglish
Title of host publicationProceedings 2018 ACM/IEEE 6th International Workshop on Green and Sustainable Software, GREENS 2018
PublisherACM, IEEE Computer Society
Pages35-42
Number of pages8
VolumePart F137724
ISBN (Electronic)9781450357326
DOIs
Publication statusPublished - 27 May 2018
Event6th ACM/IEEE International Workshop on Green and Sustainable Software, GREENS 2018, co-located with the 40th International Conference on Software Engineering, ICSE 2018 - Gothenburg, Sweden
Duration: 27 May 2018 → …

Conference

Conference6th ACM/IEEE International Workshop on Green and Sustainable Software, GREENS 2018, co-located with the 40th International Conference on Software Engineering, ICSE 2018
CountrySweden
CityGothenburg
Period27/05/18 → …

Fingerprint

Energy efficiency
Software engineering

Keywords

  • energy efficiency
  • self-adaptive software
  • systematic literature review
  • Green IT
  • Sustainability

VU Research Profile

  • Science for Sustainability
  • Connected World

Cite this

Alizadeh Moghaddam, F., Lago, P., & Ban, I. C. (2018). Self-adaptation approaches for energy efficiency: A systematic literature review. In Proceedings 2018 ACM/IEEE 6th International Workshop on Green and Sustainable Software, GREENS 2018 (Vol. Part F137724, pp. 35-42). ACM, IEEE Computer Society. https://doi.org/10.1145/3194078.3194084
Alizadeh Moghaddam, Fahimeh ; Lago, P. ; Ban, Iulia Cristina. / Self-adaptation approaches for energy efficiency : A systematic literature review. Proceedings 2018 ACM/IEEE 6th International Workshop on Green and Sustainable Software, GREENS 2018. Vol. Part F137724 ACM, IEEE Computer Society, 2018. pp. 35-42
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Alizadeh Moghaddam, F, Lago, P & Ban, IC 2018, Self-adaptation approaches for energy efficiency: A systematic literature review. in Proceedings 2018 ACM/IEEE 6th International Workshop on Green and Sustainable Software, GREENS 2018. vol. Part F137724, ACM, IEEE Computer Society, pp. 35-42, 6th ACM/IEEE International Workshop on Green and Sustainable Software, GREENS 2018, co-located with the 40th International Conference on Software Engineering, ICSE 2018, Gothenburg, Sweden, 27/05/18. https://doi.org/10.1145/3194078.3194084

Self-adaptation approaches for energy efficiency : A systematic literature review. / Alizadeh Moghaddam, Fahimeh; Lago, P.; Ban, Iulia Cristina.

Proceedings 2018 ACM/IEEE 6th International Workshop on Green and Sustainable Software, GREENS 2018. Vol. Part F137724 ACM, IEEE Computer Society, 2018. p. 35-42.

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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Alizadeh Moghaddam F, Lago P, Ban IC. Self-adaptation approaches for energy efficiency: A systematic literature review. In Proceedings 2018 ACM/IEEE 6th International Workshop on Green and Sustainable Software, GREENS 2018. Vol. Part F137724. ACM, IEEE Computer Society. 2018. p. 35-42 https://doi.org/10.1145/3194078.3194084