1 / 27

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

briar-thomas
Download Presentation

Understanding the Performance of Thin-Client Gaming

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related