Abstract
Monetizing websites and web apps through online advertising is widespread in the web ecosystem, creating a billion-dollar market. This has led to the emergence of a vast network of tertiary ad providers and ad syndication to facilitate this growing market. Nowadays, the online advertising ecosystem forces publishers to integrate ads from these third-party domains. On the one hand, this raises several privacy and security concerns that are actively being studied in recent years. On the other hand, the ability of today's browsers to load dynamic web pages with complex animations and Javascript has also transformed online advertising. This can have a significant impact on webpage performance. The latter is a critical metric for optimization since it ultimately impacts user satisfaction. Unfortunately, there are limited literature studies on understanding the performance impacts of online advertising which we argue is as important as privacy and security.
In this paper, we apply an in-depth and first-of-a-kind performance evaluation of web ads. Unlike prior efforts that rely primarily on adblockers, we perform a fine-grained analysis on the web browser's page loading process to demystify the performance cost of web ads. We aim to characterize the cost by every component of an ad, so the publisher, ad syndicate, and advertiser can improve the ad's performance with detailed guidance. For this purpose, we develop a tool, adPerf, for the Chrome browser that classifies page loading workloads into ad-related and main-content at the granularity of browser activities. Our evaluations show that online advertising entails more than 15% of browser page loading workload and approximately 88% of that is spent on JavaScript. On smartphones, this additional cost of ads is 7% lower since mobile pages include fewer and well-optimized ads. We also track the sources and delivery chain of web ads and analyze performance considering the origin of the ad contents. We observe that 2 of the well-known third-party ad domains contribute to 35% of the ads performance cost and surprisingly, top news websites implicitly include unknown third-party ads which in some cases build up to more than 37% of the ads performance cost.
- 2020. AD BLOCK DETECTION SCRIPT. https://iabtechlab.com/software/ad-block-detection-script/.Google Scholar
- 2020. Ad blocking user penetration rate in the United States. https://www.statista.com/statistics/804008/ad-blockingreach- usage-us.Google Scholar
- 2020. AdBlock. https://getadblock.com/.Google Scholar
- 2020. Adblock Plus. https://adblockplus.org.Google Scholar
- 2020. adblockparser. https://github.com/scrapinghub/adblockparser.Google Scholar
- 2020. Alexa Top News Sites. https://www.alexa.com/topsites/category/News.Google Scholar
- 2020. Alexa Top Sites. https://www.alexa.com/topsites/countries/US.Google Scholar
- 2020. Chrome DevTools Protocol. https://chromedevtools.github.io/devtools-protocol.Google Scholar
- 2020. Chrome Devtools Timeline. https://developers.google.com/web/tools/chrome-devtools/evaluate-performance/ timeline-tool.Google Scholar
- 2020. Chrome's coming changes to video ad blocking could impact YouTube. https://martechtoday.com/chromescoming- changes-to-video-ad-blocking-could-impact-youtube-238360.Google Scholar
- 2020. EasyList. https://easylist.to.Google Scholar
- 2020. Gecko profiler. https://developer.mozilla.org/en-US/docs/Mozilla/Performance/Profiling_with_the_Builtin_ Profiler.Google Scholar
- 2020. Ghostery. https://www.ghostery.com/.Google Scholar
- 2020. Global internet advertising revenue in 2015 and 2020. https://www.statista.com/statistics/237800/global-internetadvertising- revenue/.Google Scholar
- 2020. Handling Heavy Ad Interventions. https://developers.google.com/web/updates/2020/05/heavy-ad-interventions.Google Scholar
- 2020. Heavy Ads: (brief description of issue). https://bugs.chromium.org/p/chromium/issues/detail?id=1114329.Google Scholar
- 2020. Lighthouse. https://developers.google.com/web/tools/lighthouse.Google Scholar
- 2020. Loading Third-Party JavaScript. https://developers.google.com/web/fundamentals/performance/optimizingcontent- efficiency/loading-third-party-javascript/?utm_source=lighthouse&utm_medium=unknown.Google Scholar
- 2020. Number of active desktop adblock plugin users worldwide. https://www.statista.com/statistics/435252/adblockusers- worldwide/.Google Scholar
- 2020. Popeteer. https://developers.google.com/web/tools/puppeteer/get-started.Google Scholar
- 2020. The Trace Event Profiling Tool. https://www.chromium.org/developers/how- tos/trace-event-profiling-tool.Google Scholar
- 2020. uBlock. https://ublock.org/.Google Scholar
- 2020. VirusTotal. https://www.virustotal.com.Google Scholar
- 2020. Website Safety, Security Check Web Of Trust. https://www.mywot.com/.Google Scholar
- 2020. Zbrowse. https://github.com/zmap/zbrowse.Google Scholar
- Daniel An. 2018. Mobile page speed. https://www.thinkwithgoogle.com/marketing-resources/data-measurement/ mobile-page-speed-new-industry-benchmarks/.Google Scholar
- Gary Anthes. 2014. Data brokers are watching you. Commun. ACM 58, 1 (2014), 28--30.Google Scholar
Digital Library
- Muhammad Ahmad Bashir, Sajjad Arshad, William Robertson, and Christo Wilson. 2016. Tracing information flows between ad exchanges using retargeted ads. In 25th USENIX Security Symposium. 481--496. Proc. ACM Meas. Anal. Comput. Syst., Vol. 5, No. 1, Article 3. Publication date: March 2021. adPerf: Characterizing the Performance of Third-party Ads 3:25Google Scholar
- Hamad Binsalleeh. 2014. Analysis of Malware and Domain Name System Traffic. Ph.D. Dissertation. Concordia University.Google Scholar
- Michael Butkiewicz, Harsha V Madhyastha, and Vyas Sekar. 2011. Understanding website complexity: measurements, metrics, and implications. In Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference. 313--328.Google Scholar
Digital Library
- Pern Hui Chia and Svein Johan Knapskog. 2011. Re-evaluating the wisdom of crowds in assessing web security. In International Conference on Financial Cryptography and Data Security. Springer, 299--314.Google Scholar
- Charlie Curtsinger and Emery D Berger. 2015. COZ: Finding Code that Counts with Causal Profiling. In Proceedings of the 25th Symposium on Operating Systems Principles. ACM, 184--197.Google Scholar
Digital Library
- Alexandre De Corniere and Romain De Nijs. 2016. Online advertising and privacy. The RAND Journal of Economics 47, 1 (2016), 48--72.Google Scholar
Cross Ref
- Steven Englehardt and Arvind Narayanan. 2016. Online tracking: A 1-million-site measurement and analysis. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. ACM, 1388--1401.Google Scholar
Digital Library
- David S Evans. 2009. The online advertising industry: Economics, evolution, and privacy. Journal of economic perspectives 23, 3 (2009), 37--60.Google Scholar
Cross Ref
- Kiran Garimella, Orestis Kostakis, and Michael Mathioudakis. 2017. Ad-blocking: A study on performance, privacy and counter-measures. In Proceedings of the 2017 ACM on Web Science Conference. ACM, 259--262.Google Scholar
Digital Library
- Hossein Golestani, Scott Mahlke, and Satish Narayanasamy. 2019. Characterization of Unnecessary Computations in Web Applications. In 2019 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). IEEE, 11--21.Google Scholar
- Muhammad Ikram and Mohamed Ali Kaafar. 2017. A first look at mobile ad-blocking apps. In 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA). IEEE, 1--8.Google Scholar
Cross Ref
- Muhammad Ikram, Rahat Masood, Gareth Tyson, Mohamed Ali Kaafar, Noha Loizon, and Roya Ensafi. 2019. The chain of implicit trust: An analysis of the web third-party resources loading. In The World Wide Web Conference. ACM, 2851--2857.Google Scholar
Digital Library
- Umar Iqbal, Zubair Shafiq, and Zhiyun Qian. 2017. The ad wars: retrospective measurement and analysis of anti-adblock filter lists. In Proceedings of the 2017 Internet Measurement Conference. ACM, 171--183.Google Scholar
Digital Library
- Umar Iqbal, Zubair Shafiq, Peter Snyder, Shitong Zhu, Zhiyun Qian, and Benjamin Livshits. 2018. Adgraph: A machine learning approach to automatic and effective adblocking. arXiv preprint arXiv:1805.09155 (2018).Google Scholar
- Adam Lerner, Anna Kornfeld Simpson, Tadayoshi Kohno, and Franziska Roesner. 2016. Internet jones and the raiders of the lost trackers: An archaeological study of web tracking from 1996 to 2016. In 25th USENIX Security Symposium.Google Scholar
- Zhou Li, Kehuan Zhang, Yinglian Xie, Fang Yu, and XiaoFeng Wang. 2012. Knowing your enemy: understanding and detecting malicious web advertising. In Proceedings of the 2012 ACM conference on Computer and communications security. ACM, 674--686.Google Scholar
Digital Library
- Leo A Meyerovich and Rastislav Bodik. 2010. Fast and parallel webpage layout. In Proceedings of the 19th international conference on World wide web. ACM, 711--720.Google Scholar
Digital Library
- Muhammad Haris Mughees, Zhiyun Qian, and Zubair Shafiq. 2017. Detecting anti ad-blockers in the wild. Proceedings on Privacy Enhancing Technologies 2017, 3 (2017), 130--146.Google Scholar
Cross Ref
- Javad Nejati and Aruna Balasubramanian. 2016. An In-depth study of Mobile Browser Performance. In Proceedings of the 25th International Conference on World Wide Web. 1305--1315.Google Scholar
Digital Library
- Rishab Nithyanand, Sheharbano Khattak, Mobin Javed, Narseo Vallina-Rodriguez, Marjan Falahrastegar, Julia E Powles, Emiliano De Cristofaro, Hamed Haddadi, and Steven J Murdoch. 2016. Adblocking and counter blocking: A slice of the arms race. In 6th USENIX Workshop on Free and Open Communications on the Internet (FOCI 16).Google Scholar
- Behnam Pourghassemi, Ardalan Amiri Sani, and Aparna Chandramowlishwaran. 2019. What-If Analysis of Page Load Time in Web Browsers Using Causal Profiling. Proceedings of the ACM on Measurement and Analysis of Computing Systems 3, 2 (2019), 1--23.Google Scholar
Digital Library
- Behnam Pourghassemi, Ardalan Amiri Sani, and Aparna Chandramowlishwaran. 2021. Only Relative Speed Matters: Virtual Causal Profiling. ACM SIGMETRICS Performance Evaluation Review (2021).Google Scholar
- Enric Pujol, Oliver Hohlfeld, and Anja Feldmann. 2015. Annoyed users: Ads and ad-block usage in the wild. In Proceedings of the 2015 Internet Measurement Conference. ACM, 93--106.Google Scholar
Digital Library
- M Zubair Rafique, Tom Van Goethem, Wouter Joosen, Christophe Huygens, and Nick Nikiforakis. 2016. It's free for a reason: Exploring the ecosystem of free live streaming services. In Proceedings of the 23rd Network and Distributed System Security Symposium (NDSS 2016). Internet Society, 1--15.Google Scholar
- RJG Simons and Aiko Pras. 2010. The hidden energy cost of web advertising. In Proceedings of the 12th Twente Student Conference on Information Technology. 1--8.Google Scholar
- Peter Snyder, Antoine Vastel, and Ben Livshits. 2020. Who Filters the Filters: Understanding the Growth, Usefulness and Efficiency of Crowdsourced Ad Blocking. Proceedings of the ACM on Measurement and Analysis of Computing Proc. ACM Meas. Anal. Comput. Syst., Vol. 5, No. 1, Article 3. Publication date: March 2021. 3:26 Behnam Pourghassemi et al. Systems 4, 2 (2020), 1--24.Google Scholar
- Xiao Sophia Wang, Aruna Balasubramanian, Arvind Krishnamurthy, and David Wetherall. 2013. Demystifying Page Load Performance with WProf.. In 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13). 473--485.Google Scholar
- ZhenWang, Felix Xiaozhu Lin, Lin Zhong, and Mansoor Chishtie. 2011. Why Are Web Browsers Slow on Smartphones?. In Proceedings of the 12th Workshop on Mobile Computing Systems and Applications. 91--96.Google Scholar
- Craig E Wills and Doruk C Uzunoglu. 2016. What ad blockers are (and are not) doing. In 2016 Fourth IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb). IEEE, 72--77.Google Scholar
Cross Ref
- Zhonghao Yu, Sam Macbeth, Konark Modi, and Josep M Pujol. 2016. Tracking the trackers. In Proceedings of the 25th International Conference on World Wide Web. 121--132.Google Scholar
Digital Library
- Shitong Zhu, Umar Iqbal, Zhongjie Wang, Zhiyun Qian, Zubair Shafiq, and Weiteng Chen. 2019. ShadowBlock: A Lightweight and Stealthy Adblocking Browser. In The World Wide Web Conference. ACM, 2483--2493.Google Scholar
Index Terms
adPerf: Characterizing the Performance of Third-party Ads
Recommendations
adPerf: Characterizing the Performance of Third-party Ads
SIGMETRICS '21Online advertising (essentially display ads on websites) has proliferated in the last decade to the extent where it is now an integral part of the web. In this paper, we apply an in-depth and first-of-a-kind performance evaluation of web ads. Unlike ...
adPerf: Characterizing the Performance of Third-party Ads
SIGMETRICS '21: Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer SystemsOnline advertising (essentially display ads on websites) has proliferated in the last decade to the extent where it is now an integral part of the web. In this paper, we apply an in-depth and first-of-a-kind performance evaluation of web ads. Unlike ...
What-If Analysis of Page Load Time in Web Browsers Using Causal Profiling
SIGMETRICS '19: Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer SystemsWeb browsers have become one of the most commonly used applications for desktop and mobile users. Despite recent advances in network speeds and several techniques to speed up web page loading, browsers still suffer from relatively long page load time (...






Comments