arrow
返回

Characterizing Web Page Complexity and Its Impact

delete2014-06-01
delete35
PRE
AI
M
Michael Butkiewicz *
H
Harsha V. Madhyastha
V
Vyas Sekar
DOI:10.1109/TNET.2013.2269999delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
Over the years, the Web has evolved from simple text content from one server to a complex ecosystem with different types of content from servers spread across several administrative domains. There is anecdotal evidence of users being frustrated with high page load times. Because page load times are known to directly impact user satisfaction, providers would like to understand if and how the complexity of their Web sites affects the user experience. While there is an extensive literature on measuring Web graphs, Web site popularity, and the nature of Web traffic, there has been little work in understanding how complex individual Web sites are, and how this complexity impacts the clients' experience. This paper is a first step to address this gap. To this end, we identify a set of metrics to characterize the complexity of Web sites both at a content level (e.g., number and size of images) and service level (e.g., number of servers/origins). We find that the distributions of these metrics are largely independent of a Web site's popularity rank. However, some categories (e.g., News) are more complex than others. More than 60% of Web sites have content from at least five non-origin sources, and these contribute more than 35% of the bytes downloaded. In addition, we analyze which metrics are most critical for predicting page render and load times and find that the number of objects requested is the most important factor. With respect to variability in load times, however, we find that the number of servers is the best indicator.
Keyword:
Browsers
Internet
performance evaluation
Web sites
World Wide Web
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

I
IEEE-ACM Transactions on Networking
IF:
3.6
论文数:
4.4K
被引数:
9.5K

机构

U
university of california riverside
学者数:
1.1W
论文数: 8.3K
被引数: 16
University of California System 封面图
University of California System
学者数:
37.5W
论文数: 33.7W
被引数: 6.6K
引用论文

引用论文

Calcineurin‐inhibitor pain syndrome following haematopoietic stem cell transplantation
err2004-07-05
err0
PREAI
errAiko Kida; Kazuteru Ohashi; Chikako Tanaka; Noriko Kamata; Hideki Akiyama; Hisashi Sakamaki
err分享
err收藏
err分享
err收藏
err
IF0
err
err0
PREAI
err
err分享
err收藏
err
IF0
err
err0
PREAI
err
err分享
err收藏
学者 查看更多内容