Systematic Literature Review: Energy Metrics, Trade-Offs, and Best Practices in Green IT and Green Software
DOI:
https://doi.org/10.51903/zezhdt51Keywords:
Green IT, Green Software, Energy Efficiency, Performance Trade-off, Best PracticesAbstract
The rapid growth of information technology, including cloud computing, the Internet of Things, and artificial intelligence, is driving increased energy consumption and carbon emissions in the technology sector. A report by the International Energy Agency (IEA) projects that global data center electricity consumption will more than double, reaching approximately 945 TWh by 2030, or about 3% of global electricity consumption. A Deloitte study even estimates that data centers could contribute around 4% by 2030, primarily driven by the growing use of artificial intelligence. Beside physical infrastructure, software also significantly contributes to energy consumption, necessitating Green IT and Green Software approaches to reduce environmental impact. This research aims to identify and classify energy metrics used in the literature, analyze the trade-offs between energy consumption and system performance, and summarize relevant best practices for application in industrial settings. This research uses the Systematic Literature Review (SLR) method, referring to the Kitchenham protocol. The search was conducted on the IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and arXiv databases for articles published between 2021 and 2025 that included empirical data or conceptual models related to the energy efficiency of information systems. From the screening process, a number of articles were obtained and analyzed thematically. The research results show that energy metrics vary from hardware-based measurements (power meter) to estimation models (GSMM, GSMP). A trade-off was also found between energy savings and performance, necessitating a multi-objective optimization approach. This research contributes by providing a framework for classifying energy metrics, visualizing measurement method trends, and offering methodological recommendations that can be integrated into industrial practice. The implications of this research are to support digital sustainability efforts while simultaneously reducing the organization's operational costs
References
[1] I. Attabarok, S. Kamto Sudibyo, dan M. Muthohir, “Sistem Informasi Pendaftaran Pasien Rawa Jalan Berbasis Web Menggunakan Metodehot Fit Pada Puskesmas Subah Kabupaten Batang,” J. Ilm. Sist. Inf., vol. 4, no. 1, hal. 13–24, 2025.
[2] H. Jamaludin, U. Achlison, dan N. Rokhman, “Enhancing AI Model Accuracy and Scalability Through Big Data and Cloud Computing,” J. Technol. Informatics Eng., vol. 3, no. 3, hal. 296–307, 2024.
[3] M. U. Dewi, L. Santoso, dan A. B. Santoso, “Optimizing AI Performance in Industry: A Hybrid Computing Architecture Approach Based on Big Data,” J. Technol. Informatics Eng., vol. 3, no. 3, hal. 308–323, 2024.
[4] E. Azzah Rowani, D. Erwanto, dan D. A. Widhining Kusumastutie, “Monitoring Kadar Oksigen Pasien COVID-19 Untuk Isolasi Mandiri Berbasis IoT,” J. Ilm. Sist. Inf., vol. 1, no. 3, hal. 14–24, 2022.
[5] H. U. Elsa dan R. Utomo, “Menimbang Kesiapan Penerapan Carbon Pricing di Indonesia dengan Studi pada Kanada, Britania Raya, dan Australia,” J. PAJAK Indones. (Indonesian Tax Rev.., vol. 6, no. 2, hal. 410–435, 2022.
[6] International Energy Agency, Energy and AI: Energy Demand from Data Centres, AI and Cryptocurrencies. Paris: IEA, 2024. [Online]. Available: https://www.iea.org/reports/energy-and-ai
[7] A. W. Safitri, S. N. Anisa, A. Al-habsyi, T. Elektro, dan P. N. Jakarta, “Implementasi Algoritma Decision Tree dalam rangka Peningkatan Efisiensi Energi Penggunaan Beban Listrik dalam Ruangan Abstrak SNIV : SEMINAR NASIONAL INOVASI VOKASI,” Semin. Nas. Inov. Vokasi, vol. 3, no. 1, hal. 506–515, 2022.
[8] E. G. Margherita dan A. M. Braccini, “Industry 4.0 Technologies in Flexible Manufacturing for Sustainable Organizational Value: Reflections from a Multiple Case Study of Italian Manufacturers,” Inf. Syst. Front., vol. 25, no. 3, hal. 995–1016, 2023.
[9] K. J. Hoffer dan G. Savini, “Update on Intraocular Lens Power Calculation Study Protocols: The Better Way to Design and Report Clinical Trials,” Ophthalmology, vol. 128, no. 11, hal. e115–e120, 2021.
[10] A. E. I. Brownlee, J. Adair, S. O. Haraldsson, dan J. Jabbo, “Exploring the Accuracy - Energy Trade-off in Machine Learning,” Int. Work. Genet. Improv., hal. 11–18, 2021.
[11] A. Guldner et al., “Development and evaluation of a reference measurement model for assessing the resource and energy efficiency of software products and components—Green Software Measurement Model (GSMM),” Futur. Gener. Comput. Syst., vol.. 155, no. August 2023, hal. 402–418, 2024.
[12] N. Alizadeh dan F. Castor, “Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures,” Int. Conf. AI Eng. - Softw. Eng. AI, hal. 134–139, 2024.
[13] N. Tekin, A. Acar, A. Aris, A. S. Uluagac, dan V. C. Gungor, “Energy consumption of on-device machine learning models for IoT intrusion detection,” Internet of Things (Netherlands), vol. 21, no. December 2022, hal. 1–13, 2023.
[14] A. Lastovetsky dan R. R. Manumachu, “Energy-Efficient Parallel Computing: Challenges to Scaling,” Inf., vol. 14, no. 4, 2023.
[15] F. M. Hejri, C. Valmohammadi, dan M. Alborzic, “Analyzing the factors influencing green software development using a hybrid fuzzy approach,” Ijnaa.Semnan. Ac.Ir, vol. 14, no. February 2022, hal. 2008–6822, 2023.
[16] M. Artioli et al., “C6EnPLS: A High-Performance Computing Job Dataset for the Analysis of Linear Solvers’ Power Consumption,” Futur. Internet, vol. 17, no. 5, hal. 1–18, 2025.
[17] S. R. Ahmad Ibrahim, J. Yahaya, dan H. Sallehudin, “Green Software Process Factors: A Qualitative Study,” Sustain., vol. 14, no. 18, 2022.
[18] S. R. A. Ibrahim, J. Yahaya, dan H. Sallehudin, “Green Software Process Based on Sustainability, Waste and Evaluation Theory Approach: The Conceptual Model,” 2022 IEEE Int. Conf. Comput. ICOCO 2022, no. May, hal. 283–288, 2022.
[19] K. Raisian, J. Yahaya, and A. Deraman, “Green measurements for software product based on sustainability dimensions,” Comput. Syst. Sci. Eng., vol. 41, no. 1, hal. 271–288, 2022.
[20] D. Connolly Bree dan M. Ó Cinnéide, “How Software Design Affects Energy Performance: A Systematic Literature Review,” J. Softw. Evol. Process, vol. 37, no. 4, 2025.
[21] F. Castor, “Estimating the Energy Footprint of Software Systems: a Primer,” arXiv, hal. 1–24, 2024.
[22] J. Sallou, L. Cruz, dan T. Durieux, “EnergiBridge: Empowering Software Sustainability through Cross-Platform Energy Measurement,” arXiv, hal. 1–4, 2023.
[23] V. Bolón-Canedo, L. Morán-Fernández, B. Cancela, dan A. Alonso-Betanzos, “A review of Green Artificial Intelligence: Towards a more Sustainable Future,” Neurocomputing, vol. 599, no. December 2023, 2024.
[24] F. Muttaqi, M. Alfaujianto, dan A. Surahmat, “Perancangan Framework Green It Untuk Mengurangi Dampak Negatif Dalam Organisasi,” FORTECH (Journal Inf. Technol., vol. 9, no. 1, hal. 50–54, 2025.
[25] Akoh Atadoga, Uchenna Joseph Umoga, Oluwaseun Augustine Lottu, dan Enoch Oluwademilade Sodiya, “Tools, Techniques, and Trends in Sustainable Software Engineering: A Critical Review of Current Practices and Future Directions,” World J. Adv. Eng. Technol. Sci., vol. 11, no. 1, hal. 231–239, 2024.
[26] S. Berendes et al., “Evaluating the Usability of Open Source Frameworks in Energy System Modelling,” Renew. Sustain. Energy Rev., vol. 159, no. February, hal. 112174, 2022.
[27] K. M. U. Ahmed, M. H. J. Bollen, and M. Alvarez, “A Review of Data Centers Energy Consumption and Reliability Modeling,” IEEE Access, vol. 9, hal. 152536–152563, 2021.
[28] M. Koot dan F. Wijnhoven, “Usage Impact on Data Center Electricity Needs: A System Dynamic Forecasting Model,” Appl. Energy, vol. 291, no. March 2021, hal. 116798, 2021.
[29] A. Angga Mukti, D. Putri, niversitas Muhammadiyah Sukabumi, dan J. Barat, “Jurnal Rekayasa Sipil dan Arsitektur (JRSA) Optimalisasi Desain Arsitektural dengan Pendekatan Parametrik: Studi Kasus Efisiensi Struktur dalam Arsitektur Berkelanjutan,” Diterima Februari, vol. 1, no. 1, hal. 2025, 2025.
[30] A. Tabbakh, L. Al Amin, M. Islam, G. M. I. Mahmud, I. K. Chowdhury, dan M. S. H. Mukta, “Towards Sustainable AI: a Comprehensive Framework for Green AI,” Discov. Sustain., vol. 5, no. 1, 2024.
[31] S. Zaleski dan R. Michalski, “Success Factors in Sustainable Management of it Service Projects: Exploratory Factor Analysis,” Sustain., vol. 13, no. 8, 2021.
[32] G. Sartika, “Peran Strategis Manajemen Sumber Daya Manusia dalam Kesuksesan Organisasi,” J. Ekon. dan Bisnis, vol. 4, no. 1, hal. 177–188, 2024.
[33] F. Famila, G. A. K. Yudha, G. Bambulu, dan M. Veranita, “Transformasi Manajemen Rumah Sakit di Era Digital: Tinjauan Literatur dalam Fokus Pengembangan Sumber Daya Manusia,” Blantika Multidiscip. J., vol. 3, no. 8, hal. 1120–1132, 2025.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Nanik Qosidah

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All articles published in this journal are open access and licensed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).
Under this license:
-
Authors retain copyright of their work.
-
Others are free to:
-
Share — copy and redistribute the material in any medium or format;
-
Adapt — remix, transform, and build upon the material for any purpose, even commercially;
-
-
As long as they:
-
Give appropriate credit to the original author(s) and the source;
-
Provide a link to the license;
-
Indicate if changes were made;
-
Distribute their contributions under the same license as the original.
-
This ensures that derivative works will also remain free and open under the same terms.













.png)