Towards Energy Sustainability: A Literature Review of Green Software Development
Resumen
In the organizational and business environment, the increasing consumption of electrical energy by IT equipment poses a challenge in terms of cost as well as environmental impact. To address this problem, a literature review is proposed to collect and examine the most recent developments in the field of green software and their impact on energy efficiency. To carry out this literature review, the PICo search strategy was adapted and implemented and a total of 23 relevant articles were selected. In relation to the problem addressed, the concept of green software, which seeks to create efficient and sustainable programs that optimize energy consumption, has been developed. The tools and practices of sustainable software design, techniques for the development of energy-efficient systems and approaches on how to address the problems of energy consumption in data centers and cloud computing are explored.
Descargas
Citas
Abdullah, R., Abdullah, S., Din, J., & Tee, M. (2015). A systematic literature review of green software development in collaborative knowledge management environment. International Journal of Advanced Computer Technology (IJACT), 4(1), 63–80. https://www.ijact.org/ijactold/volume4issue1/IJ0410057.pdf
Akinli, S. (2013). Green software development and design for environmental sustainability. 11th International Doctoral Symposium on Empirical Software Engineering (IDoESE 2013), 9e https://www2.umbc.edu/eseiw2013/idoese/pdf/eseiw2013_IDoESE_183.pdf
Alarifi, A., Dubey, K., Amoon, M., Altameem, T., El-Samie, F. E. A., Altameem, A., Sharma, S. C., & Nasr, A. A. (2020). Energy-efficient hybrid framework for green cloud computing. IEEE Access: practical innovations, open solutions, 8, 115356–115369.
Beghoura, M. A., Boubetra, A., & Boukerram, A. (2017). Green software requirements and measurement: random decision forests-based software energy consumption profiling. Requirements Engineering, 22(1), 27–40. https://doi.org/10.1007/s00766-015-0234-2
Boyd, G., Dutrow, E., & Tunnessen, W. (2008). The evolution of the ENERGY STAR® energy performance indicator for benchmarking industrial plant manufacturing energy use. Journal of Cleaner Production, 16(6), 709–715. https://doi.org/10.1016/j.jclepro.2007.02.024
Bustamante, F. P., Guzman, C. A. P., & Vargas, J. D. L. (2014). Análisis de la aplicación del Green IT en las organizaciones. Twelfth LACCEI Latin American and Caribbean Conference for Engineering and Technology. https://www.laccei.org/LACCEI2014-Guayaquil/RefereedPapers/RP235.pdf
Capra, E., Francalanci, C., & Slaughter, S. A. (2012). Is software “green”? Application development environments and energy efficiency in open source applications. Information and Software Technology, 54(1), 60–71.
Conoci, S., Di Sanzo, P., Ciciani, B., & Quaglia, F. (2018). Adaptive performance optimization under power constraint in multi-thread applications with diverse scalability. Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering (ICPE ‘18), 16–27. https://doi.org/10.1145/3184407.3184419
Cruz, L., & Abreu, R. (2017). Performance-based guidelines for energy efficient mobile applications. 2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft), 46–57. https://doi.org/10.1109/MOBILESoft.2017.19
Dorn, J., Lacomis, J., Weimer, W., & Forrest, S. (2019). Automatically exploring tradeoffs between software output fidelity and energy costs. IEEE Transactions on Software Engineering, 45(3), 219–236. https://doi.org/10.1109/tse.2017.2775634
Garg, R., Mittal, M., & Son, L. H. (2019). Reliability and energy efficient workflow scheduling in cloud environment. Cluster Computing, 22(4), 1283–1297. https://doi.org/10.1007/s10586-019-02911-7
Georgiou, S., Rizou, S., & Spinellis, D. (2020). Software development lifecycle for energy efficiency: Techniques and tools. ACM Computing Surveys, 52(4), 1–33. https://doi.org/10.1145/3337773
Gholipour, N., Arianyan, E., & Buyya, R. (2020). A novel energy-aware resource management technique using joint VM and container consolidation approach for green computing in cloud data centers. Simulation Modelling Practice and Theory, 104. https://doi.org/10.1016/j.simpat.2020.102127
González, A. H. (2018). La sostenibilidad y el software. Dilemas contemporáneos: Educación, Política y Valores, 5(2).
Gupta, G., Singh Khichar, G., Rathi, R., & Singh, R. (2018). Energy-Aware and Cost Effective VM Allocation Strategy Based on Best Fit MultiValued Binpaking Algorithm. 2018 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 647–652.
Haddad, M., Da Costa, G., Nicod, J.-M., Péra, M.-C., Pierson, J.-M., Rehn-Sonigo, V., Stolf, P., & Varnier, C. (2021). Combined IT and power supply infrastructure sizing for standalone green data centers. Sustainable Computing: Informatics and Systems, 30.
Hedwig, M., Malkowski, S., & Neumann, D. (2009). Taming energy costs of large enterprise systems through adaptive provisioning. ICIS 2009 Proceedings, 140.
Hernández, A. (2016). ¿Por dónde empezar para aplicar prácticas verde/sostenibles en el proceso de desarrollo del software y obtener un producto verde/sostenible? Where to begin to implement practice green/sustainable in the development process of software and get a green/sustainable product? 18 Convención Científica de Ingeniería y Arquitectura, IX Simposio Universitario Iberoamericano sobre Medioambiente (SUIMA). https://www.researchgate.net/publication/312538106
Hossain, M. K., Rahman, M., Hossain, A., Rahman, S. Y., & Islam, M. M. (2020). Active & idle virtual machine migration algorithm- a new ant colony optimization approach to consolidate virtual machines and ensure green cloud computing. 2020 Emerging Technology in Computing, Communication and Electronics (ETCCE), 1–6. Hu, X., Li, P., & Sun, Y. (2021). Minimizing energy cost for green data center by exploring heterogeneous energy resource. Journal of Modern Power Systems and Clean Energy, 9(1), 148–159.
Kern, E., Dick, M., Naumann, S., & Hiller, T. (2015). Impacts of software and its engineering on the carbon footprint of ICT. Environmental empact essessment Review, 52, 53–61. https://doi.org/10.1016/j.eiar.2014.07.003
Kumar, R. (2007). Eight critical forces shape enterprise data center strategies. Gartner. https://docplayer.net/6776882-Eight-critical-forces-shape-enterprise-data-center-strategies.html
Lenka, R. K., Rath, A. K., & Sharma, S. (2019). Building reliable routing infrastructure for green IoT network. IEEE Access: practical innovations, open solutions, 7, 129892–129909.
Li, Z., Yan, C., Yu, L., & Yu, X. (2018). Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method. Future Generation Computer Systems, 80, 139–156. https://doi.org/10.1016/j.future.2017.09.075
Liu, Y., Shu, W., & Zhang, C. (2016). A parallel task scheduling optimization algorithm based on clonal operator in green cloud computing. Journal of Communications, 11(2), 85–191.
López, M., Huedo, E., & Garbajosa, J. (2019). Green IT: Tecnologías para la eficiencia energética en los sistemas TI. Universidad Politécnica de Madrid.
Mancebo, J., Arriaga, H. O., García, F., Moraga, M. Á., de Guzmán, I. G.-R., & Calero, C. (2018). EET: a device to support the measurement of software consumption. GREENS ‘18: Proceedings of the 6th International Workshop on Green and Sustainable Software, 16–22.
Mandal, R., Mondal, M. K., Banerjee, S., & Biswas, U. (2020). An approach toward design and development of an energy-aware VM selection policy with improved SLA violation in the domain of green cloud computing. The Journal of Supercomputing, 76(9), 7374–7393.
Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020). Recalibrating global data center energy-use estimates. Science, 367(6481), 984–986. https://doi.org/10.1126/science.aba3758
Microsoft Learn. (2023). Introducción a la ingeniería de software sostenible [Video]. Microsoft. https://learn.microsoft.com/es-es/training/modules/sustainable-software-engineering-overview/2-overview
Mohammadhosseini, M., Toroghi Haghighat, A., & Mahdipour, E. (2019). An efficient energy-aware method for virtual machine placement in cloud data centers using the cultural algorithm. The Journal of Supercomputing, 75, 6904–6933. https://doi.org/10.1007/s11227-019-02909-3
Munoz, D.-J., Montenegro, J. A., Pinto, M., & Fuentes, L. (2017a). Green security plugin for pervasive computing using the HADAS toolkit. 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), 796–803. https://doi.org/
Munoz, D.-J., Pinto, M., & Fuentes, L. (2017b). Green software development and research with the HADAS toolkit. ECSA ‘17: Proceedings of the 11th European Conference on Software Architecture: Companion Proceedings, 205–211. https://doi.org/10.1145/3129790.3129818
Nina, H., Pow-Sang, J. A., & Villavicencio, M. (2021). Systematic mapping of the literature on secure software development. IEEE Access: Practical Innovations, Open Solutions, 9, 36852–36867. https://doi.org/10.1109/access.2021.3062388
Padilla, A. F. A., Ávila, E. S. B., & Solórzano, R. A. T. (2023). Programación verde en el software. Revista UNESUM–Ciencias, 7(1), 187–196. https://revistas.unesum.edu.ec/index.php/unesumciencias/article/view/419
Pereira, R., Carção, T., Couto, M., Cunha, J., Fernandes, J. P., & Saraiva, J. (2017). Helping programmers improve the energy efficiency of source code. ICSE-C ‘17: 2017 Proceedings of the 39th International Conference on Software Engineering Companion, 238–240. https://doi.org/10.1109/ICSE-C.2017.80
Sahoo, S., Sahoo, B., & Turuk, A. K. (2018). An energy-efficient scheduling framework for cloud using learning automata. 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1–5. https://doi.org/10.1109/ICCCNT.2018.8493692
Venters, C. C., Seyff, N., Becker, C., Betz, S., Chitchyan, R., Duboc, L., Mcintyre, D., & Penzenstadler, B. (2017). Characterising sustainability requirements: A new species, red herring, or just an odd fish? ICSE ‘17: 39th International Conference on Software Engineering: Software Engineering in Society Track, 3–12. https://hdl.handle.net/1807/75783
Where the cloud meets the ground. (2008, October 25). The Economist. https://www.economist.com/special-report/2008/10/25/where-the-cloud-meets-the-ground
Wu, Y., Tornatore, M., Ferdousi, S., & Mukherjee, B. (2017). Green data center placement in optical cloud networks. IEEE Transactions on Green Communications and Networking, 1(3), 347–357. https://doi.org/10.1109/tgcn.2017.2709327
Yeganeh, H., Salahi, A., & Pourmina, M. A. (2019). A novel cost optimization method for mobile cloud computing by capacity planning of green data center with dynamic pricing. Canadian Journal of Electrical and Computer Engineering, 42(1), 41–51.