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![Page 1: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/1.jpg)
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Understanding Website Complexity: Measurements, Metrics, and Implications
Michael ButkiewiczHarsha Madhyastha UC Riverside
Vyas Sekar Intel Labs
![Page 2: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/2.jpg)
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doubleclick.net
cnn.comfacebook.com
cdn.turner.comads.cnn.com
Websites today are very complex!Diverse content from many servers and third party services
![Page 3: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/3.jpg)
67% of users encounter “slow” sites once a week (gomez.com)
30% > 3 Seconds
Median = 2 Seconds
Users see slow loading websites!
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![Page 4: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/4.jpg)
Why does load time matter?
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Source: gomez.com
Implications for: Website ownersEnd usersBrowser developersCustomization
![Page 5: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/5.jpg)
Our work
• Comprehensive study of website complexity– Analysis of sites across rank and category– Content and Service level metrics
• Key metrics that impact performance
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![Page 6: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/6.jpg)
Roadmap
• Introduction
• Measurement Setup
• Complexity
• Performance Implications
• Discussion and Summary
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![Page 7: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/7.jpg)
Measurement Setup
• Selecting websites– 1,700 websites from Quantcast top 20k– Primary focus on landing (home) page – Annotated with Alexa Categories
• Tools– Firefox + Firebug– No Local Caching
• Approach – 4 vantage points (3 EC2, 1 UCR)– Every 60 second one page loaded– ~30 measurements per site per vantage point over 9 weeks
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![Page 8: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/8.jpg)
Example Site Download
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CSS
Image
Script
HTML
Time
A B C DObjects
![Page 9: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/9.jpg)
"log":{ "browser":{ "name":"Firefox”, }, "pages":[{ "startedDateTime":"18:12:59.702-04:00", "title":"Wired News”, "pageTimings":{ "onLoad":4630 }] "entries":[{ "startedDateTime":"18:12:59.702-04:00", "time”:75, "request":{ ... "headers":[{ "name":"Host", "value":"www.wired.com" }, } “response":{ "content":{ "mimeType":"text/html", "size":186013, ... ...
Example Firebug Log
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![Page 10: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/10.jpg)
Roadmap
• Introduction
• Measurement Setup
• Complexity – Content-level– Service-level
• Performance Implications
• Discussion and future work
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![Page 11: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/11.jpg)
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Median Site = 57 Objects!
Number of Objects: Across Categories
Median125 Objects!
![Page 12: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/12.jpg)
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20% > 100!
Number of Objects: Across Ranks
Not as much difference across rank ranges
![Page 13: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/13.jpg)
Types of Content
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Median site: 33 Images, 10 JavaScript, 3 CSS, 0 Flash Flash
![Page 14: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/14.jpg)
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Normalized types of content
Type % Mostly Homogeneous; Flash Skewed
![Page 15: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/15.jpg)
Roadmap
• Introduction
• Measurement Setup
• Complexity – Content-level– Service-level
• Performance Implications
• Discussion and Summary
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![Page 16: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/16.jpg)
Median site requires contacting 8 servers16
Median News30 Servers!
Number of Servers
![Page 17: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/17.jpg)
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Median News20 Origins!
20% Sites > 13 Origins
Median site requires contacting 6 origins
Number of Origins
![Page 18: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/18.jpg)
Popular non-origin providersName % of sitesgoogle-analytics 58doubleclick 45quantserve 30scorecardresearch 272mdn 24googleadservices 18facebook 17yieldmanager 16
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Most common services: Analytics & AdvertisingMost common objects: Image (small!) and Javascript
bluekai.comimrworldwide.cominvitemedia.com
> 5% of sites each!
Not just usual suspects!
![Page 19: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/19.jpg)
Contribution of non-origin services
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Median SiteObjects / Bytes 30% 35%
20% sites > 80%from 3rd Party
![Page 20: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/20.jpg)
Contribution of non-origin services
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Time only 15%
Median SiteObjects / Bytes 30% 35%
80% from 3rd Party
![Page 21: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/21.jpg)
Roadmap
• Introduction
• Measurement Setup
• Complexity
• Performance Implications
• Discussion and Summary
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![Page 22: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/22.jpg)
Metric Review
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Content-Level Characteristics• Total Objects• Object Type: Number, Size• Absolute & Normalized
Service-Level Characteristics• Number: Server & Origins• Non-Origin Fraction: Servers / Objects / Time
Total combination of 33 metrics
![Page 23: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/23.jpg)
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Load Time vs. Metric Correlation
![Page 24: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/24.jpg)
Load Time Variability vs. Metrics
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![Page 25: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/25.jpg)
Roadmap
• Introduction
• Measurement Setup
• Complexity – Content-level– Service-level
• Performance Implications
• Discussion and Summary
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![Page 26: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/26.jpg)
Discussion: Many other variables
• Client-side plugins– NoScript reduces #objects by half!
• Mobile-specific customizations– Mobile version reduces #objects to a quarter
• Landing vs. non-landing pages– Non-landing seem less complex
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![Page 27: Understanding Website Complexity: Measurements, Metrics, and Implications Michael Butkiewicz Harsha Madhyastha UC Riverside 1 Vyas Sekar Intel Labs.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649cdb5503460f949a627b/html5/thumbnails/27.jpg)
Conclusions• Comprehensive study of Website Complexity
• Median site: 57 objects, contacts 8 servers across 6 origins
• Categories show more differences than popularity ranks
• Non-origin content:– Analytics & advertising popular– large # objects, bytes, servers, not time
• Key performance indicators:– Load Time # objects– Variability # servers
Data: www.cs.ucr.edu/~harsha/web_complexity
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