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Understanding the Performance of
Thin-Client Gaming
12011/5/11 CQR 2011 / Yu-Chun Chang
Yu-Chun Chang1, Po-Han Tseng2, Kuan-Ta Chen2, and Chin-Laung Lei1
1Department of Electrical Engineering, National Taiwan University
2Institute of Information Science, Academia Sinica
Outline
• Introduction
• Experiment methodology
– Experiment setup
– Performance metric extraction
• Performance evaluation
• Conclusion & future work
2011/5/11 2CQR 2011 / Yu-Chun Chang
Introduction (1/2)
3
Client Server
User’s inputs
Display updates
• Thin-client system
2011/5/11 CQR 2011 / Yu-Chun Chang
Introduction (2/2)
• Motivation
– To understand which performance metric is more
sufficient for thin-client gaming
• Frame rate, frame delay, frame loss, and etc
• Challenges
– Most thin-client products are proprietary
• Image compression, data-transmission protocol and display update
mechanism
42011/5/11 CQR 2011 / Yu-Chun Chang
Our focus
52011/5/11
QoE
Perf.
Metric
Network
Condition
Server
Client
Thin-client program
User
Network
Condition
Server
CQR 2011 / Yu-Chun Chang
QoE
Perf.
Metric
Outlines
• Introduction
• Experiment methodology
– Experiment setup
– Performance metric extraction
• Performance evaluation
• Conclusion & future work
2011/5/11 6CQR 2011 / Yu-Chun Chang
Experiment Methodology
72011/5/11
Why Do We Use Ms. Pac-Man?
• Move Pac-Man to eat pills and get the score
• Control through thin-client applications and move
Pac-Man in the game of server
– Good network condition: score↑
– Bad network condition: score↓
• Score  Quality of Experience
2011/5/11 CQR 2011 / Yu-Chun Chang 8
Ms. Pac-Man & Bot
9
• Ms. Pac-Man
– Save score after the pacman ran out of 3 lives
• Bot: ICE Pambush3 (published in IEEE CIG 2009)
– Java-based controller to move the pacman
– Capture the screen of the game and determine the position of the
pacman, ghosts, and pills
Number Score
Pill 220 10
Power pill 4 50
Ghost 4 200 (after eating power pills)
2011/5/11 CQR 2011 / Yu-Chun Chang
• Three thin-client systems
– LogMeIn
– UltraVNC
– TeamViewer
• Network conditions
10
Network condition Settings
Network delay 0 ms, 100 ms, 200 ms
Network loss rate 0%, 2.5%, 5%
Bandwidth Unlimited, 600 kbps, 300 kbps
2011/5/11 CQR 2011 / Yu-Chun Chang
• Performance metric
– Display frame rate
– Frame distortion (MSE: Mean Square Error)
• Record game play as video files in 200 FPS
112011/5/11 CQR 2011 / Yu-Chun Chang
Outlines
• Introduction
• Experiment methodology
– Experiment setup
– Performance metric extraction
• Performance evaluation
• Conclusion & future work
2011/5/11 12CQR 2011 / Yu-Chun Chang
132011/5/11
Thin Clients are Different!
CQR 2011 / Yu-Chun Chang
Visual Difference Really Matters!
142011/5/11 CQR 2011 / Yu-Chun Chang
Statistical Regression
15
Regression
Model
QoE
(score)
Independent factors
Display frame rate
Frame distortion
2011/5/11 CQR 2011 / Yu-Chun Chang
Frame-Based QoE Model
• Linear model
• QoE =
16
Adjusted R-squared: 0.72
2011/5/11 CQR 2011 / Yu-Chun Chang
Frame-Based QoE Model
17
Which Performance Metric is More Sufficient?
• QoE degradation
– Optimal user’s QoE – user’s QoE predicted by model
• Frame rate is
more sufficient!
2011/5/11 18
Frame Rate and Network Conditions
19
Network
Condition
2011/5/11
QoE
Perf.
Metric
Server
Client
Thin-client program
User
CQR 2011 / Yu-Chun Chang
The Frame Rate Prediction Model
• Frame rate =
• app1, app2: dummy variables
– LogMeIn : app1 = 1, app2 = 0
– TeamViewer : app1 = 0, app2 = 1
– UltraVNC : app1 = 0, app2 = 0
• d: delay, l: loss rate, b: bandwidth
• dl, dt, du : delay of LogMeIn, delay of TeamViewer, delay of UltraVNC
2011/5/11 20CQR 2011 / Yu-Chun Chang
The Frame Rate Prediction Model
2011/5/11 21
Adjusted R-squared: 0.85
CQR 2011 / Yu-Chun Chang
Delay of LogMeIn
Delay of UltraVNC
Bandwidth of LogMeIn
Bandwidth of UltraVNC
Predicted Frame Rate
2011/5/11 22CQR 2011 / Yu-Chun Chang
Network delay Bandwidth
Which Thin-Client is Better?
23
Network
Conditions
2011/5/11
QoE
Perf.
Metric
Server
Client
Thin-client program
User
CQR 2011 / Yu-Chun Chang
Network-Based QoE Model
• QoE =
2011/5/11
Adjusted R-squared: 0.81
The Thin-Client with Best Performance
• o symbol: empirical network condition
– 300 records collected by PingER project
2011/5/11 25CQR 2011 / Yu-Chun Chang
Conclusions & Future Work
• Display frame rate and frame distortion are both
critical to gaming performance on thin-clients
• LogMeIn performs the best among the three
implementations we studied
• Future work
– Add more thin-clients to compare the performance
– Design a generalizable experiment methodology for thin-
client gaming with different game genres
2011/5/11 26CQR 2011 / Yu-Chun Chang
Thank you for your attention!
2011/5/11 CQR 2011 / Yu-Chun Chang 27

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Understanding The Performance of Thin-Client Gaming

  • 1. Understanding the Performance of Thin-Client Gaming 12011/5/11 CQR 2011 / Yu-Chun Chang Yu-Chun Chang1, Po-Han Tseng2, Kuan-Ta Chen2, and Chin-Laung Lei1 1Department of Electrical Engineering, National Taiwan University 2Institute of Information Science, Academia Sinica
  • 2. Outline • Introduction • Experiment methodology – Experiment setup – Performance metric extraction • Performance evaluation • Conclusion & future work 2011/5/11 2CQR 2011 / Yu-Chun Chang
  • 3. Introduction (1/2) 3 Client Server User’s inputs Display updates • Thin-client system 2011/5/11 CQR 2011 / Yu-Chun Chang
  • 4. Introduction (2/2) • Motivation – To understand which performance metric is more sufficient for thin-client gaming • Frame rate, frame delay, frame loss, and etc • Challenges – Most thin-client products are proprietary • Image compression, data-transmission protocol and display update mechanism 42011/5/11 CQR 2011 / Yu-Chun Chang
  • 6. Outlines • Introduction • Experiment methodology – Experiment setup – Performance metric extraction • Performance evaluation • Conclusion & future work 2011/5/11 6CQR 2011 / Yu-Chun Chang
  • 8. Why Do We Use Ms. Pac-Man? • Move Pac-Man to eat pills and get the score • Control through thin-client applications and move Pac-Man in the game of server – Good network condition: score↑ – Bad network condition: score↓ • Score  Quality of Experience 2011/5/11 CQR 2011 / Yu-Chun Chang 8
  • 9. Ms. Pac-Man & Bot 9 • Ms. Pac-Man – Save score after the pacman ran out of 3 lives • Bot: ICE Pambush3 (published in IEEE CIG 2009) – Java-based controller to move the pacman – Capture the screen of the game and determine the position of the pacman, ghosts, and pills Number Score Pill 220 10 Power pill 4 50 Ghost 4 200 (after eating power pills) 2011/5/11 CQR 2011 / Yu-Chun Chang
  • 10. • Three thin-client systems – LogMeIn – UltraVNC – TeamViewer • Network conditions 10 Network condition Settings Network delay 0 ms, 100 ms, 200 ms Network loss rate 0%, 2.5%, 5% Bandwidth Unlimited, 600 kbps, 300 kbps 2011/5/11 CQR 2011 / Yu-Chun Chang
  • 11. • Performance metric – Display frame rate – Frame distortion (MSE: Mean Square Error) • Record game play as video files in 200 FPS 112011/5/11 CQR 2011 / Yu-Chun Chang
  • 12. Outlines • Introduction • Experiment methodology – Experiment setup – Performance metric extraction • Performance evaluation • Conclusion & future work 2011/5/11 12CQR 2011 / Yu-Chun Chang
  • 13. 132011/5/11 Thin Clients are Different! CQR 2011 / Yu-Chun Chang
  • 14. Visual Difference Really Matters! 142011/5/11 CQR 2011 / Yu-Chun Chang
  • 15. Statistical Regression 15 Regression Model QoE (score) Independent factors Display frame rate Frame distortion 2011/5/11 CQR 2011 / Yu-Chun Chang
  • 16. Frame-Based QoE Model • Linear model • QoE = 16 Adjusted R-squared: 0.72 2011/5/11 CQR 2011 / Yu-Chun Chang
  • 18. Which Performance Metric is More Sufficient? • QoE degradation – Optimal user’s QoE – user’s QoE predicted by model • Frame rate is more sufficient! 2011/5/11 18
  • 19. Frame Rate and Network Conditions 19 Network Condition 2011/5/11 QoE Perf. Metric Server Client Thin-client program User CQR 2011 / Yu-Chun Chang
  • 20. The Frame Rate Prediction Model • Frame rate = • app1, app2: dummy variables – LogMeIn : app1 = 1, app2 = 0 – TeamViewer : app1 = 0, app2 = 1 – UltraVNC : app1 = 0, app2 = 0 • d: delay, l: loss rate, b: bandwidth • dl, dt, du : delay of LogMeIn, delay of TeamViewer, delay of UltraVNC 2011/5/11 20CQR 2011 / Yu-Chun Chang
  • 21. The Frame Rate Prediction Model 2011/5/11 21 Adjusted R-squared: 0.85 CQR 2011 / Yu-Chun Chang Delay of LogMeIn Delay of UltraVNC Bandwidth of LogMeIn Bandwidth of UltraVNC
  • 22. Predicted Frame Rate 2011/5/11 22CQR 2011 / Yu-Chun Chang Network delay Bandwidth
  • 23. Which Thin-Client is Better? 23 Network Conditions 2011/5/11 QoE Perf. Metric Server Client Thin-client program User CQR 2011 / Yu-Chun Chang
  • 24. Network-Based QoE Model • QoE = 2011/5/11 Adjusted R-squared: 0.81
  • 25. The Thin-Client with Best Performance • o symbol: empirical network condition – 300 records collected by PingER project 2011/5/11 25CQR 2011 / Yu-Chun Chang
  • 26. Conclusions & Future Work • Display frame rate and frame distortion are both critical to gaming performance on thin-clients • LogMeIn performs the best among the three implementations we studied • Future work – Add more thin-clients to compare the performance – Design a generalizable experiment methodology for thin- client gaming with different game genres 2011/5/11 26CQR 2011 / Yu-Chun Chang
  • 27. Thank you for your attention! 2011/5/11 CQR 2011 / Yu-Chun Chang 27