Abstract
In this study, we explore automated reduction of the carbon footprint of web pages through genetic improvement, a process that produces alternative versions of a program by applying program transformations intended to optimize qualities of interest. We introduce a prototype tool that imposes transformations to HTML, CSS, and JavaScript code, as well as image resources, that minimize the quantity of data transferred and memory usage while also minimizing impact to the user experience (measured through loading time and number of changes imposed).
In an evaluation, our tool outperforms two baselines—the original page and randomized changes—in the average case on all projects for data transfer quantity, and 80% of projects for memory usage and load time, often with large effect size. Our results illustrate the applicability of genetic improvement to reduce the carbon footprint of web components, and offer lessons that can benefit the design of future tools.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
That yield relatively deterministic readings and are not heavily affected by the specific hardware and software configuration where the prototype tool is executed.
- 5.
Our desire was to develop a framework that could be used on any computer, without the need for dedicated equipment that measures energy consumption.
- 6.
- 7.
References
Andrae, A.S.: New perspectives on internet electricity use in 2030. Eng. Appl. Sci. Lett. 3(2), 19–31 (2020)
Bawden, T.: Global warming: data centres to consume three times as much energy in next decade experts warn (2016). https://www.independent.co.uk/climate-change/news/global-warming-data- centres-to-consume-three-times-as-much-energy-in-next-decade-experts-warn- a6830086.html. Accessed 12 Nov 2022
Bruce, B.R., Petke, J., Harman, M.: Reducing energy consumption using genetic improvement. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1327–1334 (2015)
Cao, Y., Nejati, J., Maguluri, P., Balasubramanian, A., Gandhi, A.: Analyzing the power consumption of the mobile page load. In: Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science, pp. 369–370 (2016)
Adams, T.F.C., Baouendi, R.: Website carbon calculator (2022)
De La Luz, V., Kandemir, M., Kolcu, I.: Automatic data migration for reducing energy consumption in multi-bank memory systems. In: Proceedings 2002 Design Automation Conference (IEEE Cat. No. 02CH37324), pp. 213–218. IEEE (2002)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Developers, C.: Remove unused css (2020). https://developer.chrome.com/docs/lighthouse/performance/unused-css-rules/. Accessed 09 Nov 2022
Dorn, J., Lacomis, J., Weimer, W., Forrest, S.: Automatically exploring tradeoffs between software output fidelity and energy costs. IEEE Trans. Softw. Eng. 45(3), 219–236 (2017)
Haan, K.: How many websites are there? (2023). https://www.forbes.com/advisor/business/software/website-statistics/. Accessed 04 Oct 2022
Khare, V., Yao, X., Deb, K.: Performance scaling of multi-objective evolutionary algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds.) EMO 2003. LNCS, vol. 2632, pp. 376–390. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36970-8_27
Kirupa. Running your code at the right time (2020). https://www.kirupa.com/html5/running_your_code_at_the_right_time.html. Accessed 01 Dec 2022
Lazaris, L.: An introduction and guide to the css object model (cssom) (2018). https://css-tricks.com/an-introduction-and-guide-to-the-css-object-model-cssom/. Accessed 08 Nov 2022
Manotas, I., Pollock, L., Clause, J.: Seeds: a software engineer’s energy-optimization decision support framework. In: Proceedings of the 36th International Conference on Software Engineering, pp, 503–514 (2014)
Mrazek, V., Vasicek, Z., Sekanina, L.: Evolutionary approximation of software for embedded systems: median function. In: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 795–801 (2015)
Philippot, O.: Which image format choose to reduce energy consumption and environmental impact? (2022). https://greenspector.com/en/which-image-format-to-choose-to-reduce-its-energy-consumption-and-its-environmental-impact/. Accessed 15 Jan 2023
Philippot, O., Anglade, A., Leboucq, T.: Characterization of the energy consumption of websites: impact of website implementation on resource consumption. In: ICT for Sustainability 2014 (ICT4S-2014), pp. 171–178. Atlantis Press (2014)
Sampson, A., Caşcaval, C., Ceze, L., Montesinos, P., Gracia, D.S.: Automatic discovery of performance and energy pitfalls in html and css. In: IEEE International Symposium on Workload Characterization (IISWC), pp. 82–83. IEEE (2012)
Stadnik, W., Nowak, Z.: The impact of web pages’ load time on the conversion rate of an e-commerce platform. In: Borzemski, L., Swiatek, J., Wilimowska, Z. (eds.) ISAT 2017. AISC, vol. 655, pp. 336–345. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67220-5_31
Steinke, S., Wehmeyer, L., Lee, B.-S., Marwedel, P.: Assigning program and data objects to scratchpad for energy reduction. In: Proceedings Design, Automation and Test in Europe Conference and Exhibition, pp. 409–415. IEEE (2002)
Taina, J.: How green is your software? In: Tyrväinen, P., Jansen, S., Cusumano, M.A. (eds.) ICSOB 2010. LNBIP, vol. 51, pp. 151–162. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13633-7_13
Thiagarajan, N., Aggarwal, G., Nicoara, A., Boneh, D., Singh, J.P.: Who killed my battery? analyzing mobile browser energy consumption. In: Proceedings of the 21st International Conference on World Wide Web, pp. 41–50 (2012)
White, D.R., Clark, J., Jacob, J., Poulding, S.M.: Searching for resource-efficient programs: Low-power pseudorandom number generators. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 1775–1782 (2008)
Zhu, Y., Reddi, V.J.: High-performance and energy-efficient mobile web browsing on big/little systems. In: 2013 IEEE 19th International Symposium on High Performance Computer Architecture (HPCA), pp. 13–24. IEEE (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lyu, H., Gay, G., Sakamoto, M. (2024). Exploring Genetic Improvement of the Carbon Footprint of Web Pages. In: Arcaini, P., Yue, T., Fredericks, E.M. (eds) Search-Based Software Engineering. SSBSE 2023. Lecture Notes in Computer Science, vol 14415. Springer, Cham. https://doi.org/10.1007/978-3-031-48796-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-48796-5_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48795-8
Online ISBN: 978-3-031-48796-5
eBook Packages: Computer ScienceComputer Science (R0)