community-driven adaptation iqbal mohomed department of computer science university of toronto
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Community-Driven Adaptation
Iqbal Mohomed
Department of Computer Science
University of Toronto
Mobility and Adaptation
• Content/applications target the desktop • Resource rich environment• Stable
• Mobile clients• Limited resource (nw, power, screen size)• Variable resources (Mbps to Kbps)
• Adapt application/data to bridge gap
Manual/Static Adaptation• Publishers make available content for several classes of devices
• e.g., HTML and WAP versions of Web page
• Disadvantages:
• High cost
• Several copies
• Maintaining consistency and coherence
• Continuous effort to support new types of devices
• You can never cover all possible versions!
• In practice:
• Only done for few high-traffic sites
• Limited number of devices
Automatic/Dynamic Adaptation• Adapt content on-the-fly
• Optimize for device type, user preferences, context, etc.
• Typically done using proxies
Proxy
Existing Approaches• Rule-based adaptation
• Convert images larger than 10KB to JPEGs at 25% resolution
• Constraint-based adaptation
• Functions that relate "user happiness" to metrics (resolution, color depth, frame rate, latency)
• Find point that meets all constrains and maximizes "happiness"
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Limitations• Cannot have rules/constrains per-object per-device
• Hard to define correlation between "user happiness" and metrics
• In practice, rely on small sets rules/constrains
• Based on broad generalizations
• e.g., "typical image is viewable at resolution X"
• Content agnostic
Problem
• User does not care equally about all objects
• The fidelity at which an object is useful depends a lot on the task and the object's content (semantics)
10% 10%
Problem
• User does not care equally about all objects
• The fidelity at which an object is useful depends a lot on the task and the object's content (semantics)
10% 50%
Observations
• Computers have a really hard time judging if adapted content is good enough for a task
• People can do this easily!
Have the users decide how to adapt content!
Community-Driven Adaptation
• System makes initial prediction as to how to adapt content (use rules and/or constrains)
• Let user fix adaptation decisions
• Feedback mechanism
• System learns from user feedback
• Improve adaptation prediction for future accesses
Prediction
Mobile
How it Works
Application Proxy
Server 2
Server 1Correct
Draw Backs
• User is integral part of adaptation loop
• Significant burden on user
• Iterative process is slow and frustrating
• No way people are going to accept this for every access!
Hypothesis
• User can be grouped into communities• Community members share adaptation
requirements• Adapted content that is good for one
member is likely to be good for other community members
• By tracking a few users we can learn how to adapt content for the community as a whole
Research Questions• How good are CDA predictions?
• What are good heuristics for learning how to adapt?• At what granularity should user accesses be tracked? (e.g. object, page,
site, etc.)
• How do we classify users into communities?• Does this classification change over time?
• Types of adaptations supported by this technique• Fidelity, page layout, modality (text to voice, video to image)
• UI• Good UI for working with adapted data• Effects of UI on quality of adaptation prediction
Performance Evaluation• Goal: Quantify extend to which CDA predictions
meet users’ adaptation requirements
• Approach:• Step 1: User study
• Create trace that captures levels of adaptation that users consider appropriate for a given task/content
• Step 2: Simulation• Compare rule-based and CDA predictions to
values in trace
Simples Meaningful Scenario• 1 kind of adaptation
• 1 data type
• 1 adaptation method
• 1 community
• Fidelity
• Images
• Progressive JPEG compression
• Same device
• Laptop at 56Kbps
• Same content
• Same tasks
Prototype
• Adaptation proxy• Transcode Web images into PJPEG• Split PJPEG into 10 slices
• Client• Microsoft Internet Explorer 6.0• IE plugging enables users to request fidelity refinements
• Network between client and proxy • Simulated at 56Kbps
Proxy
Prototype Operation
• When loading page, provide just 1st slice• When user clicks on image
• Provide additional slice• Reload image in IE• Add request to trace
Proxy
Web Site and TasksSite Task
Car show Find cars with license plates
eStore Buy a PDA with a camera
UofT Map Name of all buildings between two BA and Queen Subway
Goal: finish task as fast as possible (minimize clicks)
Traces capture minimum fidelity level that users’ consider to be sufficient for the task at hand.
eStore
eStore
Trace Characteristics• 77 different images
• All tasks can be performed with images available at Fidelity 4 (3 clicks)
• Average data loaded by users for all 3 tasks• 790 KB
• 32 images are never clicked by any user
Metrics• Extra data
• Measure of overshoot• Extra data sent beyond what was selected by user
• Extra clicks• Measure of undershoot• Number of time users will have to click to raise
fidelity level from prediction to what they required in trace
Results
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Results
For same clicks, 90% less extra data
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Results
For same data, 40% less extra clicks
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fixed10 fixed20 fixed40 avg med max2 upper60 max
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eStore Fidelity Breakdown
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Images
Fidelity 1 Fidelity 2 Fidelity 3 Fidelity 4 Fidelity 5
Summary
• CDA adapt data tacking into account the content’s relationship to the user task
• CDA outperforms rule-based adaptation
• 90% less bandwidth wastage
• 40% less extra clicks
Future Work• Comprehensive CDA evaluation
• More bandwidths• More devices
• Automatic classification of users into communities
• Other data types• Stored video, audio
• Other types of adaptation• Page layout, modality
• UI• Good UI for working with adapted data• Effects of UI on quality of adaptation prediction
Next 7 months
2nd & 3rd year
Research Team• Supervisor: Eyal de Lara
• Grad. Students: Iqbal MohomedAlvin Chin
• Under. Students: Jim CaiDennis Zhao