1. Network Characteristics for Server Selection in Online Games Mark Claypool Computer Science Department Worcester Polytechnic Institute Worcester, Massachusetts, USA http://www.cs.wpi.edu/~claypool/papers/game-server/

2. January 2008 MMCN, San Jose, CA, USA2 Introduction Online games growing in popularity –4 a.m.  310,000 people playing over 100,000 online games! –Game consoles & hand-helds are all network- enabled, most games online multiplayer Online games growing in variety –Then: few players on a LAN playing a FPS –Now: thousands of players on a WAN playing a FPS/RTS/MMO… To support, increasing number of game servers –Some run by players (most FPS and RTS games) –Others run by game companies Which servers do players connect to?

3. January 2008 MMCN, San Jose, CA, USA3 A Choice of Servers Players often have choices –Server browser lets player scan and select Choice matters –Some servers may require cheat protection or mods –Some maps or game types may be more fun –Some servers can be full (maximum player capacity) –Latency! Ranging from milliseconds to seconds Finding the “best” server more difficult when multiple-players trying to play together What does this server landscape look like?

4. January 2008 MMCN, San Jose, CA, USA4 This Paper 1) Better understand characteristics of game servers… –How many and how often are servers up? –Are there time of day or day of week correlations?  Query game servers over month long period (one- to-many) 2) Observe if current game server deployments sufficient for … –Games of different genres? –Single and multiple players?  Simultaneous browsing by many game clients to many game servers (many-to-many)

5. January 2008 MMCN, San Jose, CA, USA5 Outline Introduction(done) Server Browsing(next) Measurement Methodology Analysis of Results Conclusions

6. January 2008 MMCN, San Jose, CA, USA6 Game Server Browsing Game company hosts master server –Persists at well-known IP address and port Game server starts –Registers with master server Game client starts –Queries master server for list of game servers Game client queries each server –Map, players, game type … –“ping” time as a measure of latency Player selects server to play –Launches game

7. January 2008 MMCN, San Jose, CA, USA7 Typical Game Server Browser

8. January 2008 MMCN, San Jose, CA, USA8 Outline Introduction(done) Server Browsing(done) Measurement Methodology(next) Analysis of Results Conclusions

9. January 2008 MMCN, San Jose, CA, USA9 Methodology Select games (3) –id Software  Quake 3, Quake 4, Doom 3 Query master servers for selected games –1 month for long-term trends –Determine “permanent” servers Select servers (20 for each game) –Permanent and geographically distributed Emulate game browsing with Qstat –Emulate browsing of selected games –Run from command line (easy to automate) Select clients (25) –Geographically distributed, on Planet Lab Control and collect data from WPI –1 week for time of day and day of week trends

10. Geographic Location of Servers and Clients client server

11. January 2008 MMCN, San Jose, CA, USA11 Outline Introduction(done) Server Browsing(done) Measurement Methodology(done) Analysis of Results(next) –One-to-Many –Many-to-Many Conclusions

12. January 2008 MMCN, San Jose, CA, USA12 Number of Servers - Day of Week (No correlation)

13. January 2008 MMCN, San Jose, CA, USA13 Number of Servers - Time of Day (No correlation)

14. January 2008 MMCN, San Jose, CA, USA14 Servers - Permanent or Ephemeral (Three regions. Most ephemeral)

15. January 2008 MMCN, San Jose, CA, USA15 Number of Players – Percentage Filled (Few full. Many totally empty.) Average –Q3 = 1.3 –Q4 = 0.45 –D3 = 0.93

16. January 2008 MMCN, San Jose, CA, USA16 Number of Players – Day of Week (No correlation.)

17. January 2008 MMCN, San Jose, CA, USA17 Number of Players – Time of Day (Correlation.)

18. January 2008 MMCN, San Jose, CA, USA18 Latencies – Time of Day (Correlation. Min latencies good!)

19. January 2008 MMCN, San Jose, CA, USA19 Outline Introduction(done) Server Browsing(done) Measurement Methodology(done) Analysis of Results –One-to-Many(done) –Many-to-Many(next) Conclusions

20. January 2008 MMCN, San Jose, CA, USA20 Latency for Multiple Players (Lowest average latency may not be fairest.)

21. January 2008 MMCN, San Jose, CA, USA21 Maximum Latency (Curves shift right with players. Knee flattens.)

22. January 2008 MMCN, San Jose, CA, USA22 Player Performance versus Latency

23. January 2008 MMCN, San Jose, CA, USA23 Acceptable Servers (Few for First-Person, Many for Third-Person+)

24. January 2008 MMCN, San Jose, CA, USA24 Conclusions Correlation for day of week? –Server uptime (NO) –Player population (NO) Correlation for time of day? –Server uptime (NO) –Server performance (NO) –Player population (YES) Server performance depends on …? –Game generation (NO) –Number of players playing together (YES) Servers can support multiple players…? –Third-person games (YES) –First-person games (NO)

25. January 2008 MMCN, San Jose, CA, USA25 Future Work Data is public, so additional analysis possible –Latencies of connected players correlated with scores, or geography or … –Geographic location in server selection Server selection for ‘opaque’ servers –Need help from game companies Tools to improve server selection –Make it easier –Reduce network traffic –Make games more fun

26. January 2008 MMCN, San Jose, CA, USA26 Worcester, Massachusetts, USA October 21-22, 2008 http://netgames2008.cs.wpi.edu/ Game related topics in Networks and Systems (Sort of an MMCN for games!) Papers due first week of May!

27. Network Characteristics for Server Selection in Online Games Mark Claypool Computer Science Department Worcester Polytechnic Institute Worcester, Massachusetts, USA http://www.cs.wpi.edu/~claypool/papers/game-server/