Cited By
View all- Pourghassemi B(2020)Scalable Dynamic Analysis of Browsers for Privacy and PerformanceACM SIGMETRICS Performance Evaluation Review10.1145/3380908.338091547:3(20-23)Online publication date: 23-Jan-2020
adPerf: Characterizing the Performance of Third-party Ads
Authors: 
Behnam Pourghassemi, Jordan Bonecutter, Zhou Li, 
Aparna ChandramowlishwaranAuthors Info & Claims
Pages 37 - 38
Abstract
Online 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 prior efforts that rely primarily on adblockers, we perform a fine-grained analysis of 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 as we observe 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.
References
[1]
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. 2851--2857.
[2]
Behnam Pourghassemi, Ardalan Amiri Sani, and Aparna Chandramowlishwaran. 2019. What-If Analysis of Page Load Time in Web Browsers Using Causal Profiling. Proc. of the ACM on Measurement and Analysis of Computing Systems, Vol. 3, 2 (2019), 1--23.
[3]
Behnam Pourghassemi, Jordan Bonecutter, Zhou Li, and Aparna Chandramowlishwaran. 2021. adPerf: Characterizing the Performance of Third-party Ads. Proc. of the ACM on Measurement and Analysis of Computing Systems, Vol. 5, 1 (2021), 1--26.
[4]
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. 473--485.
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Information
Published In
May 2021
97 pages
ISBN:9781450380720
DOI:10.1145/3410220
- General Chair:
- Longbo Huang,
- Program Chairs:
- Anshul Gandhi,
- Negar Kiyavash,
- Jia Wang
Copyright © 2021 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Published: 06 June 2021
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SIGMETRICS '21
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SIGMETRICS '21: ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems
June 14 - 18, 2021
Virtual Event, China
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Overall Acceptance Rate 459 of 2,691 submissions, 17%
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View all- Pourghassemi B(2020)Scalable Dynamic Analysis of Browsers for Privacy and PerformanceACM SIGMETRICS Performance Evaluation Review10.1145/3380908.338091547:3(20-23)Online publication date: 23-Jan-2020
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