Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Leveraging Context-Triggered Measurements to Characterize LTE Handover Performance Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Web Performance Pitfalls

Authors : Theresa Enghardt, Thomas Zinner, Anja Feldmann

Published in: Passive and Active Measurement

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Web performance is widely studied in terms of load times, numbers of objects, object sizes, and total page sizes. However, for all these metrics, there are various definitions, data sources, and measurement tools. These often lead to different results and almost all studies do not provide sufficient details about the definition of metrics and the data sources they use. This hinders reproducibility as well as comparability of the results. This paper revisits the various definitions and quantifies their impact on performance results. To do so we assess Web metrics across a large variety of Web pages.
Amazingly, even for such “obvious” metrics as load times, differences can be huge. For example, for more than 50% of the pages, the load times vary by more than 19.1% and for 10% by more than 47% depending on the exact definition of load time. Among the main culprits for such difference are the in-/exclusion of initial redirects and the choice of data source, e.g., Resource Timings API or HTTP Archive (HAR) files. Even “simpler” metrics such as the number of objects per page have a huge variance. For the Alexa 1000, we observed a difference of more than 67 objects for 10% of the pages with a median of 7 objects. This highlights the importance of precisely specifying all metrics including how and from which data source they are computed.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter The Value of First Impressions
next chapter Characterizing Web Pornography Consumption from Passive Measurements
Appendix
Available only for authorised users
Footnotes
1
See Fig. 2 for an overview and Appendix A for more explanation.
 
2
For details regarding the methodology and the corresponding dataset see Sect. 4.
 
5
Chrome and Opera support it, Firefox is still validating their implementation.
 
8
Note that for HTTP/2, logged header sizes do not correspond to bytes on the wire anymore due to HTTP/2 header compression.
 
9
Our scripts instrument browser automation frameworks directly to give us more control and avoid the overhead of an integrated framework such as WebPagetest.
 
10
For realistic browser behavior, which includes the rendering engine, we open Web browsers including the graphical user interface rather than using them in headless mode.
 
11
Close in terms of network distance.
 
12
18. September 2018 for Alexa 1000 and 30. September 2018 for Alexa 10001–11000.
 
13
We instruct the browser automation tool to wait for the onLoad event.
 
15
Server-side redirects use HTTP status 301 or 302. Client-side redirects use status 200 and contain the redirection URL in the response content, which we do not log.
 
16
For Navigation Timings, redirects are the time between navigationStart and fetchStart. For HAR files, we use the time before the first HTTP 200 response.
 
17
In September and early October 2018, TLS 1.3 was still not deployed.
 
18
See analysis script eval/validate_object_sizes.py in our repository.
 
19
Unless the ‘Timing-Allow-Origin’ header is set, see [5].
 
20
In Firefox, only HTTP/2 Server Push objects lack body size and timings.
 
21
See the examples in Sect. 4.2 of [5].
 
22
Using the Page.frameStoppedLoading event instead did not resolve this problem.
 
23
Examples include microsoftonline.com and googleusercontent.com.
 
24
After a redirect, the browser overwrites the old fetchStart value before it fetches the new URL using a GET request. This implies that once the page load is finished, fetchStart is the start time of the loading of the final base page, as all previous values related to redirects are overwritten.
 
Literature
1.
Bocchi, E., De Cicco, L., Rossi, D.: Measuring the quality of experience of web users. In: ACM SIGCOMM Computer Communication Review, vol. 46, no. 4, pp. 8–13 (2016)
2.
3.
4.
5.
6.
7.
8.
Bruns, A., Kornstadt, A., Wichmann, D.: Web application tests with selenium. IEEE Softw. 26(5), 88–91 (2009)CrossRef
9.
10.
11.
12.
13.
Erman, J., Gopalakrishnan, V., Jana, R., Ramakrishnan, K.K.: Towards a SPDY’ier mobile web? IEEE/ACM Trans. Netw. 23(6), 2010–2023 (2015)CrossRef
14.
Qian, F., Gopalakrishnan, V., Halepovic, E., Sen, S., Spatscheck, O.: TM 3: flexible transport-layer multi-pipe multiplexing middlebox without head-of-line blocking. In: Proceedings of the 11th ACM Conference on Emerging Networking Experiments and Technologies, p. 3. ACM, New York (2015)
15.
Wang, X.S., Krishnamurthy, A., Wetherall, D.: Speeding up web page loads with Shandian. In: Proceedings of the 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2016), pp. 109–122. USENIX Association (2016)
16.
Wang, X.S., Balasubramanian, A., Krishnamurthy, A., Wetherall, D.: Demystifying page load performance with WProf. In: NSDI 2013, pp. 473–485 (2013)
17.
Butkiewicz, M., Madhyastha, H.V., Sekar, V.: Understanding website complexity: measurements, metrics, and implications. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, pp. 313–328. ACM, New York (2011)
18.
Kelton, C., Ryoo, J., Balasubramanian, A., Das, S.R.: Improving user perceived page load times using gaze. In: Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2017), pp. 545–559. USENIX Association (2017)
19.
20.
Netravali, R., Mickens, J.: Prophecy: accelerating mobile page loads using final-state write logs. In: Proceedings of the 15th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2018). USENIX Association (2018)
21.
Netravali, R., Nathan, V., Mickens, J., Balakrishnan, H.: Vesper: measuring time-to-interactivity for web pages. In: Proceedings of the 15th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2018). USENIX Association (2018)
22.
Netravali, R., Goyal, A., Mickens, J., and Balakrishnan, H.: Polaris: faster page loads using fine-grained dependency tracking. In: Proceedings of the 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2016). USENIX Association (2016)
23.
Zaki, Y., Chen, J., Pötsch, T., Ahmad, T., Subramanian, L.: Dissecting web latency in Ghana. In: Proceedings of the 2014 Conference on Internet Measurement Conference, pp. 241–248. ACM, New York (2014)
24.
Han, B., Qian, F., Hao, S., Ji, L.: An anatomy of mobile web performance over multipath TCP. In: Proceedings of the 11th ACM Conference on Emerging Networking Experiments and Technologies, p. 5. ACM, New York (2015)
25.
Naylor, D., et al.: The cost of the S in HTTPS. In: Proceedings of the 10th ACM International on Conference on Emerging Networking Experiments and Technologies, pp. 133–140. ACM, New York (2014)
26.
Scheitle, Q., et al.: A long way to the top: significance, structure, and stability of internet top lists. In: Internet Measurement Conference 2018. ACM, New York (2018)
27.
28.
Egger, S., Hossfeld, T., Schatz, R., Fiedler, M.: Waiting times in quality of experience for web based services. In: 2012 Fourth International Workshop on Quality of Multimedia Experience (QoMEX), pp. 86–96. IEEE (2012)
29.
Barth, A.: The web origin concept. RFC 6454 (2011)
Metadata
Title
Web Performance Pitfalls
Authors
Theresa Enghardt
Thomas Zinner
Anja Feldmann
Copyright Year
2019
Book
Passive and Active Measurement

Print ISBN: 978-3-030-15985-6

Electronic ISBN: 978-3-030-15986-3

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-15986-3_19

Premium Partner

    Image Credits