Network Characteristics for Server Selection in Online Games

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. 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? MMCN, San Jose, CA, USA

3. 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? MMCN, San Jose, CA, USA

4. 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) MMCN, San Jose, CA, USA

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

6. 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 MMCN, San Jose, CA, USA

7. Typical Game Server Browser MMCN, San Jose, CA, USA

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

9. 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 MMCN, San Jose, CA, USA

10. Geographic Location of Servers and Clients server client

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

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

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

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

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

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

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

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

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

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

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

22. Player Performance versus Latency MMCN, San Jose, CA, USA

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

24. 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) MMCN, San Jose, CA, USA

25. 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 MMCN, San Jose, CA, USA

26. 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! MMCN, San Jose, CA, USA

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/