1. A Traffic Characterization of Popular On-Line Games Wu-Chang Feng, Francis Chang, Wu- Chi Feng, and Jonathan Walpole IEEE/ACM Trans. Networking, Jun. 2005

2. Outline System architecture Trace summary Periodicity and predictability Tiny packets Client characterization Conclusions The bad news The good news

3. System architecture (1/2) Server Player REG global state local state

4. System architecture (2/2) Server Player REG global state local state

5. Counter-Strike network trace information

6. Counter-Strike trace network statistics

7. Per-minute bandwidth during trace A lot of short- term variation!

8. Per-minute packet load during trace A lot of short- term variation!

9. Per-minute number of players for entire trace A lot of short- term variation!

10. Total packet load for the m = 10 ms interval size Busty & periodic! [due to the game logic] The game server deterministically floods its clients with state updates about every 50 ms.

11. Incoming packet load for the m = 10 ms interval size Because clients are heterogeneous, incoming packet load is not highly synchronized.

12. Outgoing packet load for the m = 10 ms interval size Outgoing packet load is highly synchronized due to the game logic.

13. Total packet load plot for m = 50 ms The high frequency variability is smoothed. (large sampling period) The low frequency variability is caused by: map changes and round time limits (30-min)

14. Total packet load plot for m = 1 s Noticeable dips appear every 30 min intervals with smaller variations occurring at a higher frequency.

15. Total packet load plot for m = 30 min The low frequency variability is smoothed.

16. Client bandwidth histogram 80%: 56 Kb modem

17. Packet size PDF (probability density function)

18. Client characterization Session-time distribution Weibull distribution Geographic distribution

19. Session time CDF (cumulative density function)

20. Fitted Weibull distribution on session time PDF

21. Why not exponential distribution? The shape of the exponential distribution is completely determined by the failure rate. However, the failure rate is not constant for shorter session times, thus making it difficult to accurately fit it to an exponential distribution. Short flows could be: Players browse the server ’ s features. A plug-in used to kick players for killing their teammates.

22. Player failure rates for individual session times

23. Longitude histogram for players server

24. Longitude CDF for players

25. Player locations over time A global positioning and repositioning of resources over time is required.

26. Average great-circle distance of players over time

27. The bad news On-line game traffic consists of large, periodic bursts of short packets. The implications for designers of networking devices: Have sufficient forwarding capacity to handle small packet sizes Employ ECN (prevent from packet loss) Have small buffers (delayed packets are worse than drop packets) Employ active queue management (operate over short queues)

28. The good news The predictability in resource requirements makes the modeling, simulation, and provisioning on-line gaming traffic a relatively simple task.