Analyzing the Influence of Online Advertisements on ISP Network Traffic
Resumo
With the constant internet access increase and with a large offer of content, a priori free of charge, there has been a significant increase in data consumption on the network, and online advertising follows this trend. Online advertising allows advertisers to leverage their capital through its platform and allows the dissemination of products and services within the network. However, the advertises load, which, for the most part, is unwanted for users, impacts internet access plans contracted by customers. This work aims to measure the impact of such advertisements on network traffic. We rely on two network traces to characterize consumption patterns: one from a regional access ISP with 33 customers and 15 days of capture and one from a publicly available dataset with 2000 customers and 3 minutes of capture. The results show that at least 11% of all the traffic volume relates to advertising content on the regional ISP and 38% of the public dataset.
Referências
(2024). MAWI Working Group Traffic Archive — mawi.wide.ad.jp. [link]. [Accessed 04-04-2024].
(2024). PAN-OS — docs.paloaltonetworks.com. [link]. [Accessed 04-04-2024].
(2024). Pi-hole &x2013; Network-wide Ad Blocking — pi-hole.net. [link]. [Accessed 04-04-2024].
Aseri, M., Dawande, M., Janakiraman, G., and S. Mookerjee, V. (2020). Ad-blockers: A blessing or a curse? Information Systems Research, 31(2):627–646.
Black, S. (2024). Unified hosts file with base extensions. [link]. [Accessed 12-04-2024].
Chillemi, O., Galavotti, S., and Gui, B. (2020). The impact of data caps on mobile broadband internet access: A welfare analysis. Information Economics and Policy, 50:100843.
Feldmann, A., Gasser, O., Lichtblau, F., Pujol, E., Poese, I., Dietzel, C., Wagner, D., Wichtlhuber, M., Tapiador, J., Vallina-Rodriguez, N., et al. (2020). The lockdown effect: Implications of the covid-19 pandemic on internet traffic. In Proceedings of the ACM internet measurement conference, pages 1–18.
Gao, C., Zeng, J., Sarro, F., Lo, D., King, I., and Lyu, M. R. (2021). Do users care about ad’s performance costs? exploring the effects of the performance costs of in-app ads on user experience. Information and Software Technology, 132:106471.
Goldfarb, A. and Tucker, C. (2019). Digital economics. Journal of Economic Literature, 57(1):3–43.
Gupta, R. and Panda, R. (2020). Block the blocker: Studying the effects of anti ad-blocking. arXiv preprint arXiv:2001.09434.
Hoffman, P. E. (2023). DNS Security Extensions (DNSSEC). RFC 9364.
Huitema, C., Dickinson, S., and Mankin, A. (2022). DNS over Dedicated QUIC Connections. RFC 9250.
Kurose, J. F. and Ross, K. (2021). Computer networking: A top-down approach 8th edition. Pearson.
Mazhar, M. H. and Shafiq, Z. (2018). Real-time video quality of experience monitoring for https and quic. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications, pages 1331–1339. IEEE.
Mouawi, R., Elhajj, I. H., Chehab, A., and Kayssi, A. (2015). Comparison of in-app ads traffic in different ad networks. In 2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pages 581–587.
Pujol, E., Hohlfeld, O., and Feldmann, A. (2015). Annoyed users: Ads and ad-block usage in the wild. In Proceedings of the 2015 Internet Measurement Conference, IMC ’15, page 93–106, New York, NY, USA. Association for Computing Machinery.
Würfel, M., Han, Q., and Kaiser, M. (2021). Online advertising revenue forecasting: An interpretable deep learning approach. In 2021 IEEE International Conference on Big Data (Big Data), pages 1980–1989.
Yar, M. A. (2023). Internet traffic data set. [link].
