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Dissecting the protocol and network traffic of the OnLive cloud gaming platform

Abstract

Cloud gaming is a new paradigm that is envisaged to play a pivotal role in the video game industry in forthcoming years. Cloud gaming, or gaming on demand, is a type of online gaming that allows on-demand streaming of game content onto non-specialised devices (e.g. PC, smart TV, etc.). This approach requires no downloads or game installation because the actual game is executed on the game company’s server and is streamed directly to the client. Nonetheless, this revolutionary approach significantly affects the network load generated by online games. As cloud gaming presents new challenges for both network engineers and the research community, both groups need to be fully conversant with these new cloud gaming platforms. The purpose of this paper is to investigate OnLive, one of the most popular cloud gaming platforms. Our key contributions are: (a) a review of the state-of-the-art of cloud gaming; (b) reverse engineering of the OnLive protocol; and (c) a synthetic traffic model for OnLive.

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Notes

  1. To simplify the description of the OnLive session, we will name the different protocols and roles of OnLive servers that have been identified, although the different roles may be performed by the same physical server.

  2. While we have identified more fields and the different message types of this custom protocol, they are not described here for sake of clarity.

  3. This port is dynamic and larger than the OnLive’s 16,384 default one. The port number is notified to the client using the custom protocol encapsulated in echo RTP messages from the server.

  4. Interestingly, the muting audio option does not interrupt the audio flows, but simply notifies the client not to play them.

  5. http://www.wireshark.org/.

  6. http://www.pingtest.net/.

  7. http://www.speedtest.net/.

  8. http://www.netfilter.org/projects/iptables.

  9. This video frame splitting is performed at the application layer, that is, several RTP messages are generated, instead of a single UDP packet being fragmented by the IP layer.

  10. http://www.minitab.com/.

References

  1. Avallone, S., Emma, D., Pescapé, A., Ventre, G.: Performance evaluation of an open distributed platform for realistic traffic generation. Perform. Eval. 60(1–4), 359–392 (2005)

    Article  Google Scholar 

  2. Borella, M.S.: Source models of network game traffic. Comput. Commun. 23(4), 403–410 (2000)

    Article  Google Scholar 

  3. But, J., Armitage, G., Stewart, L.: Outsourcing automated QoS control of home routers for a better online game experience. Commun. Mag. 46(12), 64–70 (2008)

    Article  Google Scholar 

  4. Cevizci, I., Erol, M., Oktug, S.F.: Analysis of multi-player online game traffic based on self-similarity. In: Proceedings of 5th ACM SIGCOMM Workshop on Network and System Support for Games (2006)

  5. Chen, K.T., Chang, Y.C., Tseng, P.H., Huang, C.Y., Lei, C.L.: Measuring the latency of cloud gaming systems. In: Proceedings of ACM Multimedia (2011)

  6. Cheng, L., Bhushan, A., Pajarola, R., Zarki, M.E.: Real-time 3D graphics streaming using MPEG-4. In: Proceedings of the IEEE/ACM Workshop on Broadband Wireless Services and Applications (2004)

  7. Choy, S., Wong, B., Simon, G., Rosenberg, C.: The brewing storm in cloud gaming: A measurement study on cloud to end-user latency. In: Proceedings of the 11th ACM/IEEE Annual Workshop on Network and Systems Support for Games (2012)

  8. Huang, C.Y., Hsu, C.H., Chang, Y.C., Chen, K.T.: Gaminganywhere: an open cloud gaming system. In: Proceedings of ACM SIGMM Conference on Multimedia Systems (MMSys’13) (2013)

  9. Cisco Systems Inc: Cisco Visual Networking Index: Forecast and Methodology, pp. 2011–2016 (White paper) (2012)

  10. Claypool, M., Claypool, K.T.: Latency and player actions in online games. Commun. ACM 49(11), 40–45 (2006)

    Article  Google Scholar 

  11. Claypool, M., Finkel, D., Grant, A., Solano, M.: Thin to win? Network performance analysis of the OnLive thin client game system. In: Proceedings of the 11th ACM/IEEE Annual Workshop on Network and Systems Support for Games (2012)

  12. Cricenti, A., Branch, P.: ARMA(1,1) modeling of Quake4 server to client game traffic. In: Proceedings of the 6th ACM SIGCOMM Workshop on Network and System Support for Games, pp. 70–74 (2007)

  13. Dainotti, A., Pescapé, A., Ventre, G.: A packet-level traffic model of Starcraft. In: Proceedings of the Second International Workshop on Hot Topics in Peer-to-Peer Systems, pp. 33–42 (2005)

  14. DFC Intelligence. http://www.dfcint.com/wp/?p=277. Accessed Online 27 March 2013

  15. Färber, J.: Network game traffic modelling. In: Proceedings of the 1st Workshop on Network and System Support for Games, pp. 53–57 (2002)

  16. Feng, W.C., Chang, F., Feng, W.C., Walpole, J.: A traffic characterization of popular on-line games. IEEE/ACM Trans. Netw. 13(3), 488–500 (2005)

    Article  Google Scholar 

  17. Goldmann, M., Kreitz, G.: Measurements on the Spotify peer-assisted music-on-demand streaming system. In: Proceedings of IEEE International Conference on Peer-to-Peer, Computing, pp. 206–211 (2011)

  18. Hedayat, K. et al.: A two-way active measurement protocol (TWAMP) (2008) (updated by RFC 5618)

  19. Humphreys, G., Houston, M., Ng, R., Frank, R., Ahern, S., Kirchner, P.D., Klosowski, J.T.: Chromium: a stream-processing framework for interactive rendering on clusters. ACM Trans. Graph. 21, 693–702 (2002)

    Article  Google Scholar 

  20. Karachristos, T., Apostolatos, D., Metafas, D.: A real-time streaming games-on-demand system. In: Proceedings of the 3rd International Conference on Digital Interactive Media in Entertainment and Arts, pp. 51–56 (2008)

  21. Kim, J., Hong, E., Choi, J.: Measurement and Analysis of a Massively Multiplayer Online Role Playing Game Traffic. In: Proceedings of Advanced Network Conference, pp. 1–8 (2003)

  22. Kim, S., Kim, K., Won, J.: Multi-view rendering approach for cloud-based gaming services. In: Proceedings of the 3rd International Conference on Advances in Future Internet, pp. 102–107 (2011)

  23. KwangSik, S., et al.: Transformation approach to model online gaming traffic. ETRI J. 33(2), 219–229 (2011)

    Article  Google Scholar 

  24. Lakkakorpi, J., Heiner, A., Ruutuc, J.: Measurement and characterization of Internet gaming traffic. In: Technical Report, Espoo, Finland. Research Seminar on Networking (2002)

  25. Lang, T., Armitage, G.: An ns2 model for the Xbox system link game Halo. Tech. Rep. 030613A, Swinburne University of Technology, Faculty of Information and Communication Technologies, Center for Advanced Internet Architectures (2003)

  26. Lang, T., Armitage, G., Branch, P., Choo, H.Y.: A synthetic traffic model for half-life. In: Proceedings of the Australian Telecommunications, Networks and Applications Conference (2003)

  27. Lang, T., Branch, P., Armitage, G.: A synthetic traffic model for Quake3. In: Proceedings of the 2004 ACM SIGCHI International Conference on Advances in computer entertainment technology, pp. 233–238 (2004)

  28. Lee, Y.T., Chen, K.T., Su, H.I., Lei, C.L.: Are all games equally cloud-gaming-friendly? An Electromyographic Approach. In: Proceedings of the 11th IEEE/ACM Annual Workshop on Network and Systems Support for Games (2012)

  29. Manzano, M., Hernandez, J.A., Urueña, M., Calle, E.: An empirical study of cloud gaming. In: Proceedings of the 11th ACM/IEEE Annual Workshop on Network and Systems Support for Games (2012)

  30. Odlyzko, A.M.: Internet traffic growth: sources and implications. In: Proceedings of SPIE, pp. 1–15 (2003)

  31. OnLive. http://www.onlive.com. Accessed Online 27 February 2013

  32. Park, H., Kim, T., Kim, S.: Network traffic analysis and modeling for games. Internet and Network Economics. Lecture Notes in Computer Science, pp. 1056–1065. Springer, Berlin (2005)

  33. Paxson, V.: Empirically-derived analytic models of wide-area TCP connections. IEEE/ACM Trans. Netw. 2(2), 316–336 (1994)

    Article  Google Scholar 

  34. Pederson, S.P., Johnson, M.E.: Estimating model discrepancy. Technometrics 32(3), 305–314 (1990)

    Article  Google Scholar 

  35. Piri, E., Hirvonen, M., Laulajainen, J.P.: Empirical evaluation of streamed online gaming over WiMAX. In: Proceedings of the 8th International ICST Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, pp. 255–270 (2012)

  36. Ratti, S., Hariri, B., Shirmohammadi, S.: A survey of first-person shooter gaming traffic on the internet. IEEE Internet Comput. 14(5), 60–69 (2010)

    Article  Google Scholar 

  37. Sandvine: Global internet phenomena report. In: Technical Report (2013)

  38. Shalunov, S. et al.: A one-way active measurement protocol (OWAMP) (2006)

  39. Shea, R., Liu, J., Ngai, E.C.H., Cui, Y.: Cloud gaming: architecture and performance. IEEE Netw. 27(4), 16–21 (2013)

    Google Scholar 

  40. Suznjevic, M., Dobrijevic, O., Matijasevic, M.: MMORPG player actions: network performance, session patterns and latency requirements analysis. Multimed. Tools Appl. 45(1–3), 191–241 (2009)

    Article  Google Scholar 

  41. Suznjevic, M., Stupar, I., Matijasevic, M.: A model and software architecture for MMORPG traffic generation based on player behavior. Multimed. Syst. (2012)

  42. Svoboda, P., Karner, W., Rupp, M.: Traffic analysis and modeling for world of warcraft. In: Proceedings of the IEEE International Conference on Communications, pp. 1612–1617 (2007)

  43. The Telegraph: http://www.telegraph.co.uk/technology/. Accessed Online 27 March 2013

  44. Wang, X., Kim, H., Vasilakos, A.V., Kwon, T.T., Choi, Y., Choi, S., Jang, H.: Measurement and analysis of world of warcraft in mobile WiMAX networks. In: Proceedings of the 8th Workshop on Network and System Support for Games, p. 6 (2009)

  45. Winter, D.D., Simoens, P., Deboosere, L., Turck, F.D., Moreau, J., Dhoedt, B., Demeester, P.: A hybrid thin-client protocol for multimedia streaming and interactive gaming applications. In: Proceedings of the international workshop on Network and Operating Systems Support for Digital Audio and Video, p. 15 (2006)

  46. Wu, Y., Huang, H., Zhang, D.: Traffic modeling for massive multiplayer on-line role playing game (MMORPG) in GPRS access network. In: Proceedings of the International Conference on Communications, Circuits and Systems Proceedings, pp. 1811–1815 (2006)

  47. Yoo, C.S.: Cloud computing: architectural and policy implications. Rev. Ind. Organ. 38(4), 405–421 (2011)

    Article  Google Scholar 

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Acknowledgments

This work was partly supported by the projects: TEC 2012-32336, TEC 2010-12250-E and S-2009/TIC-1468, and by the Generalitat de Catalunya through the research support program project SGR-1202 and AGAUR FI-DGR 2012 grant. Also, this work was supported by the research project 036-0362027-1639, by the Ministry of Science, Education, and Sports of the Republic of Croatia, and Ericsson Nikola Tesla, Zagreb, Croatia. The research leading to these results has also received funding from the European Community’s Seventh Framework Programme under grant agreement no. 285939 (ACROSS).

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Manzano, M., Urueña, M., Sužnjević, M. et al. Dissecting the protocol and network traffic of the OnLive cloud gaming platform. Multimedia Systems 20, 451–470 (2014). https://doi.org/10.1007/s00530-014-0370-4

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Keywords

  • Cloud gaming
  • Online games
  • OnLive
  • Protocol
  • Reverse engineering
  • Traffic modelling