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Understanding the Performance of Thin-Client Gaming

Understanding the Performance of Thin-Client Gaming. Yu-Chun Chang 1 , Po-Han Tseng 2 , Kuan -Ta Chen 2 , and Chin- Laung Lei 1 1 Department of Electrical Engineering, National Taiwan University 2 Institute of Information Science, Academia Sinica. Outline. Introduction

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Understanding the Performance of Thin-Client Gaming

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  1. Understanding the Performance of Thin-Client Gaming 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 CQR 2011 / Yu-Chun Chang

  2. Outline • Introduction • Experiment methodology • Experiment setup • Performance metric extraction • Performance evaluation • Conclusion & future work CQR 2011 / Yu-Chun Chang

  3. Introduction (1/2) • Thin-client system User’s inputs Display updates Client Server 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 CQR 2011 / Yu-Chun Chang

  5. Our focus Thin-client program NetworkCondition NetworkCondition Perf. Metric QoE Perf. Metric QoE User Server Server Client CQR 2011 / Yu-Chun Chang

  6. Outlines • Introduction • Experiment methodology • Experiment setup • Performance metric extraction • Performance evaluation • Conclusion & future work CQR 2011 / Yu-Chun Chang

  7. Experiment Methodology

  8. Why 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 CQR 2011 / Yu-Chun Chang

  9. Ms. Pac-Man & Bot • 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 CQR 2011 / Yu-Chun Chang

  10. Three thin-client systems • LogMeIn • UltraVNC • TeamViewer • Network conditions 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 CQR 2011 / Yu-Chun Chang

  12. Outlines • Introduction • Experiment methodology • Experiment setup • Performance metric extraction • Performance evaluation • Conclusion & future work CQR 2011 / Yu-Chun Chang

  13. Thin Clients are Different! CQR 2011 / Yu-Chun Chang

  14. Visual Difference Really Matters! CQR 2011 / Yu-Chun Chang

  15. Statistical Regression Independent factors Display frame rate Frame distortion Regression Model QoE (score) CQR 2011 / Yu-Chun Chang

  16. Frame-Based QoE Model • Linear model • QoE = Adjusted R-squared: 0.72 CQR 2011 / Yu-Chun Chang

  17. Frame-Based QoE Model

  18. Which Performance Metric is More Sufficient? • QoE degradation • Optimal user’s QoE – user’s QoE predicted by model • Frame rate is more sufficient!

  19. Frame Rate and Network Conditions Thin-client program NetworkCondition Perf. Metric QoE Server User Client 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 CQR 2011 / Yu-Chun Chang

  21. The Frame Rate Prediction Model Delay of LogMeIn Delay of UltraVNC Bandwidth of LogMeIn Bandwidth of UltraVNC Adjusted R-squared: 0.85 CQR 2011 / Yu-Chun Chang

  22. Predicted Frame Rate Network delay Bandwidth CQR 2011 / Yu-Chun Chang

  23. Which Thin-Client is Better? Thin-client program NetworkConditions Perf. Metric QoE Server User Client CQR 2011 / Yu-Chun Chang

  24. Network-Based QoE Model • QoE = Adjusted R-squared: 0.81

  25. The Thin-Client with Best Performance • o symbol: empirical network condition • 300 records collected by PingER project CQR 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 see comparisons of performance • Design a generalizable experiment methodology for thin-client gaming with different game genres CQR 2011 / Yu-Chun Chang

  27. Thank you for your attention! CQR 2011 / Yu-Chun Chang

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