Enabling the Transition to the Mobile Web with WebSieve Michael Butkiewicz, Zhe Wu, Shunan Li,...

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Enabling the Transition to the Mobile Web with WebSieve

Michael Butkiewicz, Zhe Wu, Shunan Li, Pavithra Murali, Vagelis Hristidis,

Harsha V. Madhyastha (UC Riverside), Vyas Sekar (Stony Brook University)

Source: gomez.com

Why does load time matter?

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Source: gomez.com

Why does load time matter?

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“Google incorporates site speed into search rankings”

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High Load Times are Common

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Median Sitew/o mobile version

19 sec

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Median Mobile Site

13 sec

Median Sitew/o mobile version

19 sec

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Page Complexity Load Time

Well StudiedMobiSys 2010 Huang, et al.

IMC 2011 Butkiewicz, et al.

HotMobile 2011 Wang, et al.

httparchive.org

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httparchive.org

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200850 objects




httparchive.org

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httparchive.org 200850 objects

2012100 objects!

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httparchive.org 200850 objects

2012100 objects!

Webpage Complexity outpacing Mobile Resources

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WebSieve’s GoalState of the Art: Manual Reduction of Complexity

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Our Goal: Automate Mobile-Friendly Optimization

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– Block Low Importance Objects



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– Block Low Importance Objects– Prioritize High Importance Objects



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Challenge 1. Inferring Object Utility

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Low Utility

Low Utility Challenge 1. Inferring Object UtilityLow

Utility

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High Utility

High Utility


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? Utility


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HTML

CSS JS

Challenge 2. Discovering Dependencies

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HTML

CSS JS


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HTML

CSS JS


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Challenge 3. Predicting New Load Times

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Server Variability


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Script Processing

Server Variability


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ParallelConnection

Limits

Script Processing

Server Variability


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foo.com/xO1 O2 O3 O4

Front-end Back-end

Our Vision For WebSieve

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Dependency Extraction


Front-end Back-end


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User Utility Inference

ObjectSelection


Front-end Back-end


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ObjectSelection



Fetch Fingerprint

Fetch HTML

Front-end Back-end

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ObjectSelection


Front-end Back-end


{O1,O3}

Fetch HTML

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ObjectSelection


Front-end Back-end


2. Load {O1,O3}

1. HTML Received

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Challenge 1. Inferring Utility

Approach: User Study + Machine Learning

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Challenge 1. Inferring Utility

Approach: User Study + Machine Learning

Simple Machine Learning Not Enough

Personalization Is Key

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HTML

JPG a JS 1

JS 2 JPG b

JPG c

Original Load

Challenge 2. Discover Dependencies

Approach: Block & Infer

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HTML

JPG a JS 1

JS 2 JPG b

JPG c

HTML

JPG a

Original Load Block JS 1



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HTML

JPG a JS 1

JS 2 JPG b

JPG c

HTML

JPG a

HTML

JPG a JS 1

JPG b

Original Load Block JS 1 Block JS 2



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HTML

JPG a JS 1

JS 2 JPG b

JPG c

HTML

JPG a

HTML

JPG a JS 1

JPG b

Original Load Block JS 1 Block JS 2



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Challenge 3. Object Selection

Approach: Page Tree Cut

Util: 5Time: 4s

Util: 2Time: 0.5s

Util: 1Time: 1s

Util: 0Time: 0.1s

Util: 1Time: 2s

Util: 2Time: 0.5s

Util: 5Time: 0.5s

Util: 7Time: 0.4s

Util: 3Time: 2s

• Cut Dependency DAG• Maximize Utility• Respect Load Time Budget

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Challenge 3. Object Selection

Approach: Page Tree Cut

Util: 5Time: 4s

Util: 2Time: 0.5s

Util: 1Time: 1s

Util: 0Time: 0.1s

Util: 1Time: 2s

Util: 2Time: 0.5s

Util: 5Time: 0.5s

Util: 7Time: 0.4s

Util: 3Time: 2s

• Cut Dependency DAG• Maximize Utility• Respect Load Time Budget

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Wrap UpLoad Time Strongly Impacts User Satisfaction

State of the Art: Manual Complexity Reduction

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State of the Art: Manual Complexity ReductionWebSieve: Automate Mobile-Friendly Optimization

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– Block Low Importance Objects

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Challenges: Object Dependency, Utility, Obj. Selection

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Challenges: Object Dependency, Utility, Obj. Selection

Ongoing Prototype Development & Promising Results

Enabling the Transition to the Mobile Web with WebSieve Michael Butkiewicz, Zhe Wu, Shunan Li,...

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