Improving Web Site Performance
Using Edge Servers in Fog
Computing Architecture
Jiang Zhu, Douglas S. Chan, Mythili S. Pra...
2
Source http://www.stevesouders.com/blog/2012/02/10/the-performance-golden-rule/
3
November 2012

November 2013
* Chart produced by httparchive.org

4
5
• Zoompf - analysis tools, recommendations
• Strangeloop Site Optimizer – appliance, external instance,

software on web s...
7
8
1.
2.
3.
4.
5.

Choose PNG over GIF (lossless)
Crush your PNGs (lossless)
Strip needless JPEG metadata (lossless)
Scale ge...
Recommendations show a possible 90% reduction in page size
10
Firefox add-on
11
Analysis shows nearly 70% reduction possible using compression

12
GET /v-app/scripts/107652916-dom.common.js HTTP/1.1
Host: www.blogger.com
User-Agent: Mozilla/5.0 (…) Gecko/2008070208 Fir...
• Pro:

•

smaller transfer size

• Con:

•

CPU cycles – on client and server

• Don’t compress resources < 1K
• Don’t co...
15
16
The Cloud and the Fog
Actors (end
users and
providers)

Scalability,
security,
availability

Enterprises,
individuals

ren...
• Optimizing for client device
Adapt optimization filters for a specific client device display resolution
Adapt optimizati...
19
Fog node
running web
optimization
service

www.a.com

Fog node
with web
optimization
service

20
Fog Device

User, Client
Metadata
Distributed
data store

Optimization
Engine
Optimization
config

Web Proxy +
Optimizatio...
22
• Overall process flow

Initial and subsequent requests
• Dynamic and custom optimizations

Client device and local networ...
Category

Filters

Caching
Optimizations

canonicalize javascript libraries, extend cache, extend cache pdfs,
local storag...
7000

6000

4000

No Optimizations
3000

Minimized Round Trip Time

2000

Performance
improvement of
1.5x

1000

0
1
4
7
1...
10000
9000
8000

6000
5000
No Optimization
4000
Optimized Browser Rendering

3000
2000
1000
0
1
4
7
10
13
16
19
22
25
28
3...
9000

8000

6000

5000
Optimized Browser Rendering

4000

Minimized Round Trip Time
3000

2000

1000

0
1
4
7
10
13
16
19
...
• Proof of Concept

• Testing on various client platforms
• Tuning the optimization engine
• Real data results

28
Thank you.
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Improving Web Siste Performance Using Edge Services in Fog Computing Architecture

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We consider web optimization within Fog Computing context. We apply existing methods for web optimization in a novel manner, such that these methods can be combined with unique knowledge that is only available at the edge (Fog) nodes. More dynamic adaptation to the user’s conditions (eg. network status and device’s computing load) can also be accomplished with network edge specific knowledge. As a result, a user’s webpage rendering performance is improved beyond that achieved by simply applying those methods at the webserver or CDNs.

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Improving Web Siste Performance Using Edge Services in Fog Computing Architecture

  1. 1. Improving Web Site Performance Using Edge Servers in Fog Computing Architecture Jiang Zhu, Douglas S. Chan, Mythili S. Prabhu, Preethi Natarajan, Hao Hu, Nii Ampa-Sowa Advanced Architecture & Research Cisco Systems, Inc CTECH 2013 © 2010 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 1
  2. 2. 2
  3. 3. Source http://www.stevesouders.com/blog/2012/02/10/the-performance-golden-rule/ 3
  4. 4. November 2012 November 2013 * Chart produced by httparchive.org 4
  5. 5. 5
  6. 6. • Zoompf - analysis tools, recommendations • Strangeloop Site Optimizer – appliance, external instance, software on web server • Stingray Aptimize - runs as a proxy • Torbit – site optimizer service 6
  7. 7. 7
  8. 8. 8
  9. 9. 1. 2. 3. 4. 5. Choose PNG over GIF (lossless) Crush your PNGs (lossless) Strip needless JPEG metadata (lossless) Scale generated images (lossless) Image maps 9
  10. 10. Recommendations show a possible 90% reduction in page size 10
  11. 11. Firefox add-on 11
  12. 12. Analysis shows nearly 70% reduction possible using compression 12
  13. 13. GET /v-app/scripts/107652916-dom.common.js HTTP/1.1 Host: www.blogger.com User-Agent: Mozilla/5.0 (…) Gecko/2008070208 Firefox/3.0.1 Accept-Encoding: gzip,deflate HTTP/1.1 200 OK Content-Type: application/x-javascript Last-Modified: Mon, 22 Sep 2008 21:14:35 GMT Content-Length: 2066 6230 Content-Encoding: gzip function d(s) {... XmoÛHþÿ FÖvã*wØoq... typically reduces size by 70% (6230-2066)/6230 = 67% 13
  14. 14. • Pro: • smaller transfer size • Con: • CPU cycles – on client and server • Don’t compress resources < 1K • Don’t compress resources already compressed (images) 14
  15. 15. 15
  16. 16. 16
  17. 17. The Cloud and the Fog Actors (end users and providers) Scalability, security, availability Enterprises, individuals rendering, transcoding Low latency end subscribers, service providers Fusion/aggre gation, MR Geodistribution, security, privacy Control loops Partitioning/ quasiautonomy, security Software frameworks MR/Hadoop DC Bursting Deep data mining, search, financial algorithms Web Hosting E-commerce Web applications Key Use Cases Large Batch Jobs Cloud Computing at the Core Key requirements Areas of application Storage Mobile Content Delivery Geo-distributed Sensor/Actuator Networks Smart cities, environmental monitoring Large Scale Distributed Controlled Systems Fog Computing Video streaming, gaming SCV, Smart Grid, Int. Transportation © 2010 Cisco and/or its affiliates. All rights reserved. Utilities, federal/state agencies, car manufacturers Cisco Confidential 17
  18. 18. • Optimizing for client device Adapt optimization filters for a specific client device display resolution Adapt optimization filters for a specific client browser • Optimizing for client network Gather network characteristics from Fog device on the edge Adapt optimization for network characteristics (wired vs. wireless) 18
  19. 19. 19
  20. 20. Fog node running web optimization service www.a.com Fog node with web optimization service 20
  21. 21. Fog Device User, Client Metadata Distributed data store Optimization Engine Optimization config Web Proxy + Optimization Filters Performance feedback Browser on Client Request/ Optimized resource 21
  22. 22. 22
  23. 23. • Overall process flow Initial and subsequent requests • Dynamic and custom optimizations Client device and local network conditions • Per user optimization Tracking users via MAC/ IP addresses • Obtaining and applying user experience Collective and individual experience 23
  24. 24. Category Filters Caching Optimizations canonicalize javascript libraries, extend cache, extend cache pdfs, local storage cache, outline css, outline javascript Minimize Round Trip Times combine css, flatten css imports, inline css, combine javascript, inline javascript, move css above scripts, insert dns prefetch, sprite images Minimize Payload Size collapse whitespace, combine heads, elide attributes, rewrite javascript, rewrite images, remove comments, remove quotes, rewrite css, trim urls, fallback rewrite css urls, rewrite style attributes, rewrite style attributes with url Minimize Request Overhead rewrite domains Optimize Browser Rendering convert meta tags, defer javascript, inline preview images, resize mobile images lazyload images, move css to head, rewrite images, rewrite style attributes rewrite style attributes with url 24
  25. 25. 7000 6000 4000 No Optimizations 3000 Minimized Round Trip Time 2000 Performance improvement of 1.5x 1000 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 Page Load Time (ms) 5000 Request Index 25
  26. 26. 10000 9000 8000 6000 5000 No Optimization 4000 Optimized Browser Rendering 3000 2000 1000 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 Page Load Time (ms) 7000 Performance improvement of 2x Request Index 26
  27. 27. 9000 8000 6000 5000 Optimized Browser Rendering 4000 Minimized Round Trip Time 3000 2000 1000 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 Page Load Time (ms) 7000 Request Index 27
  28. 28. • Proof of Concept • Testing on various client platforms • Tuning the optimization engine • Real data results 28
  29. 29. Thank you.

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