1. On the Geographic Distribution of On- line Game Servers and Players Wu-chang FengWu-chi Feng Discussion moderated By: John Carter

2. Motivation  Understanding geographic distribution of game servers and players. Q: How much can we generalize results?  Server placement strategies enhance gaming experience. FPSes require proximity to servers. Q: Is geographic proximity a good approximation for topological proximity?

3. Contributions  Geographic characterization of game servers for popular FPSes: Q: Where are the servers located ?  Geographic characterization of game players for one game server: Q: Where are the players?

4. Methodology  Q: How did the researchers measure the client and server locations? Q: How did they determine IP addresses? Q: How did they map addresses to locs?  Q: How did researchers encourage players to come to their server?

5. Methodology  Obtaining server addresses: Obtain server list from centralized registry server. Select server based on round trip latency from client, using ping.

6. Methodology (contd.)  Obtaining client addresses. Not easy! Needs access to centralized authentication and game servers. Easier to focus on the distribution of players for a particular game.  Deriving geographic locations Use commercial geographic mapping tool.

7. Evaluation  Longitudinal histogram for Game Servers distribution.

8. Evaluation (contd.)  Longitudinal CDF of game server distribution.

9. Evaluation (contd.)  Latitudinal CDF of game servers.

10. Evaluation (contd.)  Longitudinal histogram for players.

11. Evaluation (contd.)  Longitude CDF for players. Only 30% of the players are within 10 degree longitude and about 50% players are in North America!

12. Discussion  Q: What did they believe caused the distributions of clients/servers?  Q: How did the distribution change over time? Time of day? Load?  Q: What can servers/developers do to improve client choices?

13. Evaluation (contd.)  Reasons for utilization of game servers by geographically remote users: Disparity between geographic location and network topology. Application server delays dominate network delays. Server selection mechanisms for popular games are broken. The number of players on a server determines desirability over delay. A shortage of servers overseas.

14. Evaluation (contd.)  Player locations over time – Time of day phenomenon!

15. Evaluation (contd.)  Player locations over time.

16. Conclusion.  Results show that game servers are mainly distributed across the northern hemisphere in US, Europe and East Asia.  There is some geographic preference between players and servers.  The dominant factor in the geographic distribution of game players is the time- of-day.

17. Provisioning On-line Games: Analysis of a Busy Counter-Strike Server Francis Chang, Wu-chang Feng, Wu-chi Feng, Jonathan Walpole

18. Week in the Life of a C-S Server  Multiple transient clients connect to pre- existing stationary game server Internet Server Network Monitor

19. Discussion  Q: What were the major findings? Bandwidth patterns? Periodic? Client bandwidth? Packet sizes?  Q: Is game traffic like other traffic? How so or how not?  Q: What lessons can we learn?

20. Bandwidth: Nearly constant

21. Usage patterns  Network usage unlike traditional internet apps Bandwidth is not self-similar, aggregates well

22. Bandwidth analysis  Highly periodic broadcasts at msec level

23. Client bandwidth  Designed to saturate the “last mile” Carefully kept under 56Kbps modem limit

24. Packet size distributions  Primarily long bursts of small packets Driven by low latency requirements

25. Final observations  Bursts of small packets not a great match for way routers work Hard to quickly route large bursts  NAT routers particularly bad Drop lots of packets  Unfortunately, buffering is not the solution Added latency eliminates benefit

26. Discussion What lessons did we learn?

27. What would YOU do?  What if you were building a server infrastructure for an online game?  How would you build it?  What issues would you consider?  How would you optimize for various user scenarios?  Can we improve network proximity?  Let’s design some “on paper”…