Android Workload Suite - Measure the user interactions of...
Transcript of Android Workload Suite - Measure the user interactions of...
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Xiao-Feng Li
Oct, 2011
Android Workload Suite (AWS): Measure the software stack of mobile devices
Thanks to Greg Zhu and Ke Chen
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Summary
• Android Workload Suite (AWS) is an engineering tool for Android software stack measurement
– It uses the software stack metrics to measure the interaction scenarios
• AWS covers the major areas for Android software stack evaluation
– The key is to map user interactions to system behavior
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Agenda
• User interactions measurement
• Interaction scenarios definition
– Case studies
• Android workloads construction
– Case studies
• Summary
• Information
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Optimize User Interaction Systematically
• What we need:
– A well-established methodology
– An engineering workload suite
– An analysis/tuning toolkit
– Sightings/requests/feedbacks from PECA/IXR, xPGs, developers, users, etc.
• (The methodology details are in another deck)
• (The UXtune toolkit details are in another deck)
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User Interactions with Client Device
• A sequence of interactions
Human Input
Target Response
Screen transition
Object movement
• Does the input trigger the target correctly?
• Does the system act responsively?
• Does the graphics transition smoothly?
• Does the object move coherently?
Device User
interaction
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Interaction Measurement Aspects
• User controls device (subject object)
1. Accuracy/fuzziness: Range/resolution of inputs that can trigger a correct response
2. Coherence: Object move delay, difference in move trajectory
• Device reacts to user (object subject)
3. Responsiveness: Time between an input delivered to the device response, and to the action finish
4. Smoothness: Maximal frame time, frame time variance, FPS and frame drop rate
Device User
interaction
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Android Workload Suite (AWS)
• Goals
– Reflect the representative usage of Android client devices
– Evaluate Performance, Power and User interactions
• AWS usages
– Drive and validate Android optimizations
– Support comparative and competitive analysis
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AWS 2.0 Suite Workload #Scenarios Components
Browser
Media
Graphics
Productivity
Touch
Sensors
Built-in apps
Task management
AWS
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Agenda
• User interactions measurement
• Interaction scenarios definition
– Case studies
• Android workloads construction
– Case studies
• Summary
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Understand The Representative Scenarios
• Extensive surveys
– Feedbacks/inputs from users
– Public documents from key players
– Popular applications
– Form-factor usages (Tablet vs. smart-phone)
– User interaction life-cycles and software design
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Usage Categories: Market and Built-in Apps
Home screen, App launcher, Setting, Touch, Sensor…
Phone, Contacts, SMS, MMS, E-mail, IM, Video phone…
Internet access, Video, Music, Gaming, eBooks…
Office, Video conference, Payment, LBS, Security…
Basic accessibility
Communication
Information & Content
Business & Productivity
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Tablet-specific Apps Characteristics
• Larger screen size than phone
– More realistic view experience (game, cartoon, 3D)
– Easier or more controls through touch/sensors or virtual controllers (virtual controller, editor, handwriting)
– Bigger space to put more contents (news, education, ebook)
– Support more than one players (game, education)
– PC-experience web access (browser, info portal)
– More small utilities apps for daily use (on-screen vs. in-pocket)
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Phone-specific Apps Characteristics
• Phone as handy gadget as a Swiss-knife
– Communicator (chat through AV/text/picture)
– Camera (barcode scanner and photo/video apps)
– Utility (flashlight, night vision, barcode scanner)
– Navigation (GPS, compass), music player, Phone
• Smaller size
– Games are cartoon or lightweight-animation based
– Relatively simple games with simple sensor controls
– Many accelerometer-based games
• Shake to operate (vs. gyroscope-based with Tablet)
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Form Factor Consideration in Workload Design
• Some scenarios in AWS may only exist in one form factor, e.g.,
– Status bar vs. system bar
– Browser: switch window vs. switch tab
• Same scenario in AWS may have two design variants, e.g.,
– The 2D game workload has more animated sprites in its tablet profile
– Browser workload use PC web page on tablet, and _can_ use mobile web page on phone
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User Scenario Categories
• User operations – Browsing, gaming, authoring, setting/configuring
• Touch gestures, and sensors
– Communications
• Loading and rendering – Loading:
• Web page, eBook, image
– Rendering: • Web page, HTML5, eBook, media, 2D/3D
• Task management – App launch, Task switch – Multi tasking (Parallel execution)
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Primary Metrics for User Scenarios
• User operations
– Browsing, gaming, authoring, setting, communication
– Responsiveness, smoothness, coherency, accuracy
• Loading and rendering – Web/HTML5, eBook, media, image, 2D/3D
– Responsiveness (loading time, rendering capability), smoothness, coherency, accuracy
• Task management
– App launch, Task switch, Multi tasking
– Responsiveness (time to launch/exit), smoothness, coherency, accuracy
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Agenda
• User interactions measurement
• Interaction scenarios definition
– Case studies
• Android workloads construction
– Case studies
• Summary
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Example of Interaction Lifecycle - Browser
User interaction lifecycle is composed with three types of scenarios:
User operations Loading and rendering Task management
Launch browser
(loading time)
Input URL
(responsiveness)
webpage loading
(loading time)
Read webpage
Scroll/Fling/Zoom
webpage
(responsiveness,
smoothness)
Open new tab
(responsiveness)
Switch tab
(responsiveness)
Exit browser
(loading time)
Scenarios on critical path are selected
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Example of Interaction Lifecycle - Video Player
Time
Loading and rendering Task management
Touch thumbnail to Play
(startup time)
Seek forward/backward
while playing (seek response time)
Normal playback (Smoothness,
dropped frames)
Exit player (unloading time)
Pause/Resume (resume response
time)
Play next video clip (switch response
time)
User operations
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Agenda
• User interactions measurement
• Interaction scenarios definition
– Case studies
• Android workloads construction
– Case studies
• Summary
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Interaction Measurement Criteria
• Measure the critical path of user interactions in software stack
• Criteria
– Perceivable (PECA/IXR has the UX perceptual model)
– Measureable (by different teams)
– Repeatable (in multiple measurements)
– Comparable (between different measured systems)
– Reasonable (about the causality)
– Verifiable (for an optimization)
– Automatable (largely unattended, not strictly)
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Workloads Construction
• Key is to map user interactions to system behavior
– Purpose is to assist software optimization instead of simulating user behavior
• Kinds of workloads
– Standalone workload: Run as full workload and give results
– Micro workload: Stress certain execution paths of the stack
– Measurement tool: Allow manual operation and get metrics
– Scenario driver of built-in app: only give inputs and
extract metrics
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Activity 1 Activity 2
Service 1 Service 2
Input output
Activity 1
Activity 2
Service 1
Service 2
Input
output
1. Standalone workload
Activity 1
Activity 2
Service 1
Service 2
Input
output
2. Micro workload
Activity 1
Activity 2
Service 1
Service 2
Input
output
3. Measurement tool
Activity 1
Activity 2
Service 1
Service 2
Input
output
4. Scenario driver
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Kinds of Workloads
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Challenges in Workload Construction
• How to measure response time of user inputs?
• How to measure smoothness?
• How to measure drag coherence?
• How to make the results repeatable?
• How to make the workload comparable across platforms?
• Etc.
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Challenge1: Response Time Measurement
• Software latency is our optimization focus
– Software latency is around x100ms
– Touch sampling rate is typically 200HZ (5ms interval)
Typically 200Hz sampling rate
Input-Gestures Manual touch
Touch sensor
Event hub
Input dispatcher
app Input driver
Event dev file
Software latency Physical latency
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Challenge2: Smoothness Measurement Notice the followings: • Max frame time • #frames > 30ms • Frame time variance • FPS
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Challenge3: Drag Coherence Measurement
Distances[k] = {Touch[i].pos – Draw[k].pos | Touch[i].t<=Draw[k+1].t AND Touch[i].t>Draw[k].t}
Coherency = Max(,Max(Distances*k+) | k=0,…,N-)
Event1 Event2 Event3 … EventX
Input raw events
Browser events Event1 Event2/3 Event k ……
Frame1 Frame2
Time
……
EventY
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Challenge4: Repeatable Results
• Use Input-Gesture tool to generate standard touch gestures for inputs
• Ensure the generated gestures are comparable across different platforms
1000000000 3 48 1 1000000010 3 53 3284 1000000020 3 54 2747 1000000030 0 2 0 1000000040 0 0 0 1000005000 3 48 1 1000005010 3 53 3284 1000005020 3 54 2735
1000000000 3 48 1 1000000010 3 53 1810 1000000020 3 54 1515 1000000030 0 2 0 1000000040 0 0 0 1000005000 3 48 1 1000005010 3 53 1810 1000005020 3 54 1508
Events of same gesture on Device X
Events of same gesture on Device Y
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Challenge5: Comparable Across Platforms
• For example, browser workloads
– Different platforms may have different built-in browsers
• Depending on the measurement purpose
– If for rendering engine comparison, use standard contents (web pages or Javascripts)
– If for app operation comparison, use “scenario driver” generated by input-Gestures
– If for framework comparison, build a “standalone browser” and install to target platforms
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Agenda
• User interactions measurement
• Interaction scenarios definition
– Case studies
• Android workloads construction
– Case studies
• Summary
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Workload Construction Case Studies
• Browser scroll scenario
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Browser Scroll Scenario
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Time T0
Time T2
Position P0
Time T1
P1
P2
P1 1.finger starts
3. finger moves, content moves
2. content starts to move
Time T3
P3
4. finger releases
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Measurement for Scroll
• Response time
– How fast the content start to follow the finger
• Drag lag distance
– How far the content movement lags behind finger
• Smoothness
– How smooth the browser animates the scroll
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Software Stack Internals in Scroll
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Event1 EventM EventN … EventX
Input raw events
Browser events
ACTION DOWN
ACTION
MOVE
ACTION
MOVE ……
Frame1
Time
……
EventY
ACTION
MOVE
…
Browser drawing
Detects Scroll Gesture
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Response Time Measurement
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Event1 EventM EventN … EventX
Input raw events
Browser events
ACTION DOWN
ACTION
MOVE
ACTION
MOVE ……
Frame1
Time
……
EventY
ACTION
MOVE
…
Browser drawing
Detects Scroll Gesture
(Δx, Δy: offset from ACTION_DOWN)
First event send time
First frame drawn time Response Time
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Smoothness Measurement
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EventX
Input raw events
ACTION
MOVE
ACTION
MOVE
Time
EventY
Browser drawing
Browser events
ACTION
UP
EventZ
First Frame
… … Frame m Frame n … … Last
frame
….
….
T1
T2
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Drag Lag Measurement
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Distances[k] = {Touch[i].pos – Draw[k].pos | Touch[i].t<=Draw[k+1].t AND Touch[i].t>Draw[k].t}
Coherency = Max(,Max(Distances*k+) | k=0,…,N-)
Event1 Event2 Event3 … EventX
Input raw events
Browser events Event1 Event2/3 Event k ……
Frame1 Frame2
Time
……
EventY
Browser drawing
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Results Repeatability
• Standard scroll gesture set generated by the Input-Gestures tool
– Scroll up 20 times, down 20 times
– Events are transformed for different devices
gesture duration: 900ms
gesture duration: 900ms
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Workload Usage
• Support built-in and self-built browser
• Support scenario selection
• Support user input webpage address
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Detailed Results Archive
• Result Files - /data/local/tmp/XXX_result.txt
– Record data of each gesture
• Frame interval, maximum LTF, #LTFs
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Agenda
• User interactions measurement
• Interaction scenarios definition
– Case studies
• Android workloads construction
– Case studies
• Summary
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Summary
• Android Workload Suite (AWS) is an engineering tool for Android software stack measurement
– It uses the software stack metrics to measure the interaction scenarios
• AWS covers the major areas for Android software stack evaluation
– The key is to map user interactions to system behavior
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