The Sustainable Software Development Practices
Keywords:
Software development, Green SDLC, resource optimization, software sustainability, energy-efficiency codingAbstract
Rapid software industry growth has brought significant technological advancement and a great deal of environmental concerns regarding energy consumption, resource usage, and carbon emissions. The area of sustainable software development practices has emerged in recent decades as an important one for addressing these environmental concerns. This research project explores how sustainability principles can be integrated into the Software Development Lifecycle (SDLC), focusing on energy-efficient coding techniques, sustainable architecture patterns, and resource optimization during deployment and maintenance. The Green SDLC model proposed herein outlines a structured approach for reducing the ecological footprint of software systems without sacrificing performance and scalability. Using a combination of literature review, practical experimentation, and case study analysis, this research identifies influential methodologies that developers and organizations can implement to reduce their software’s environmental impact. Experiments utilizing tools such as GreenMeter and Joulemeter to measure energy consumption and resource efficiency across different software implementations. Case studies conducted by industry leaders such as Google and Spotify further demonstrate the feasibility and benefits of sustainable software practices in reducing energy consumption and carbon dioxide emissions.
The findings of this project prove that sustainable software development is shaping the future of the tech industry by promoting greener and more energy-efficient solutions for software development. Green SDLC guides developers in shaping their contributions to a sustainable digital future; technological progress will be brought together with environmental care. Further research is recommended to unify sustainability metrics and investigate recent technologies, such as artificial intelligence (AI) and blockchain, for enhancing sustainability in software development.
Keywords: software development, Green SDLC, resource optimization, software sustainability, energy-efficiency coding.
References
Buyya, R., Srirama, S. N., Casale, G., Calheiros, R., Simmhan, Y., Varghese, B., ... & Bahsoon, R. (2024). Energy‐efficiency and sustainability in new generation cloud computing: A vision and directions for integrated management of data centre resources and workloads. Software: Practice and Experience. https://onlinelibrary.wiley.com/doi/10.1002/spe.3248
Duboc, L., Penzenstadler, B., Porras, J., Akinli Kocak, S., Betz, S., Chitchyan, R., ... & Venters, C. C. (2020). Requirements engineering for sustainability: An awareness framework for designing software systems for a better tomorrow. Requirements Engineering, 25, 469–492. https://link.springer.com/article/10.1007/s00766-020-00336-y
Fawole, A.A., Orikpete, O.F., Ehiobu, N.N. et al. (2023). Climate change implications of electronic waste: strategies for sustainable management. Bulletin of National Research Centre, 47, 147. https://bnrc.springeropen.com/articles/10.1186/s42269-023-01124-8
Georgiou, S., Rizou, S., & Spinellis, D. (2019). Software development lifecycle for energy efficiency: Techniques and tools. ACM Computing Surveys, 52(4), 1–33. https://dl.acm.org/doi/abs/10.1145/3337773
Giacobbe, M., Celesti, A., Fazio, M., Villari, M., & Puliafito, A. (2015, September). A sustainable energy-aware resource management strategy for IoT cloud federation. In 2015 IEEE International Symposium on Systems Engineering (ISSE) (pp. 170–175). IEEE. https://ieeexplore.ieee.org/abstract/document/7302751
Gill, S. S., Tuli, S., Toosi, A. N., Cuadrado, F., Garraghan, P., Bahsoon, R., ... & Buyya, R. (2020). ThermoSim: Deep learning-based framework for modeling and simulation of thermal-aware resource management for cloud computing environments. Journal of Systems and Software, 166, 110596. https://www.sciencedirect.com/science/article/abs/pii/S0164121220300753
Groza, C., Dumitru-Cristian, A., Marcu, M., & Bogdan, R. (2024). A developer-oriented framework for assessing power consumption in mobile applications: Android energy smells case study. Sensors, 24(19), 6469. https://www.mdpi.com/1424-8220/24/19/6469
Guldner, A., Bender, R., Calero, C., Fernando, G. S., Funke, M., Gröger, J., ... & Naumann, S. (2024). 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). Future Generation Computer Systems, 155, 402–418. https://www.sciencedirect.com/science/article/pii/S0167739X24000384
Katal, A., Dahiya, S., & Choudhury, T. (2023). Energy efficiency in cloud computing data centers: A survey on software technologies. Cluster Computing, 26(3), 1845-1875. https://link.springer.com/article/10.1007/s10586-022-03713-0
Kumar, Y., Kaul, S., & Hu, Y. C. (2022). Machine learning for energy-resource allocation, workflow scheduling, and live migration in cloud computing: State-of-the-art survey. Sustainable Computing: Informatics and Systems, 36, 100780. https://www.sciencedirect.com/science/article/abs/pii/S2210537922001111
Li, Y., Wen, Y., Tao, D., & Guan, K. (2019). Transforming cooling optimization for green data center via deep reinforcement learning. IEEE Transactions on Cybernetics, 50(5), 2002–2013. https://ieeexplore.ieee.org/abstract/document/8772127
Muralidhar, R., Borovica-Gajic, R., & Buyya, R. (2022). Energy-efficient computing systems: Architectures, abstractions, and modeling to techniques and standards. ACM Computing Surveys, 54(11s), 1–37. https://dl.acm.org/doi/abs/10.1145/3511094
Omrany, H., Soebarto, V., Zuo, J., & Chang, R. (2021). A comprehensive framework for standardizing system boundary definition in life cycle energy assessments. Buildings, 11(6), 230. https://www.mdpi.com/2075-5309/11/6/230
Pang, J. (2024). Exploring the nexus of community college faculty and the actual application of generative artificial intelligence technologies in courses and syllabi. (Doctoral dissertation, National Louis University Dissertations. https://digitalcommons.nl.edu/diss/804/
Pereira, R., Couto, M., Saraiva, J., Cunha, J., & Fernandes, J. P. (2016, May). The influence of the Java collection framework on overall energy consumption. In Proceedings of the 5th International Workshop on Green and Sustainable Software (pp. 15-21). ACM. https://sci-hub.se/https://doi.org/10.1145/2896967.2896968
Procaccianti, G., Fernández, H., & Lago, P. (2016). Empirical evaluation of two best practices for energy-efficient software development. Journal of Systems and Software, 117, 185-198. https://www.sciencedirect.com/science/article/abs/pii/S0164121216000777
Shahzad, M., Qu, Y., Rehman, S. U., & Zafar, A. U. (2022). Adoption of green innovation technology to accelerate sustainable development among manufacturing industry. Journal of Innovation & Knowledge, 7(4), 100231. https://www.sciencedirect.com/science/article/pii/S2444569X22000671
Shan, S., Genç, S. Y., Kamran, H. W., & Dinca, G. (2021). Role of green technology innovation and renewable energy in carbon neutrality: A sustainable investigation from Turkey. Journal of Environmental Management, 294, 113004. https://www.sciencedirect.com/science/article/pii/S0301479721010665
S. Singh, A. Tiwari, S. Rastogi and V. Sharma, "Green and Sustainable Software Model for IT Enterprises," 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2021, pp. 1157-1161, doi: 10.1109/ICECA52323.2021.9675938. https://ieeexplore.ieee.org/document/9675938/references#references
Singh, R., Akram, S. V., Gehlot, A., Buddhi, D., Priyadarshi, N., & Twala, B. (2022). Energy System 4.0: Digitalization of the energy sector with inclination towards sustainability. Sensors, 22(17), 6619. https://www.mdpi.com/1424-8220/22/17/6619
Venters, C. C., Capilla, R., Betz, S., Penzenstadler, B., Crick, T., Crouch, S., ... & Carrillo, C. (2018). Software sustainability: Research and practice from a software architecture viewpoint. Journal of Systems and Software, 138, 174-188. https://www.sciencedirect.com/science/article/abs/pii/S0164121217303072
Zhang, X., Wu, T., Chen, M., Wei, T., Zhou, J., Hu, S., & Buyya, R. (2019). Energy-aware virtual machine allocation for cloud with resource reservation. Journal of Systems and Software, 147, 147-161. https://www.sciencedirect.com/science/article/abs/pii/S0164121218302152
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