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Video Game Traffic Profile

Video Game Traffic Profile

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Video Game Traffic Profile

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  1. Video Game Traffic Profile Glenn Harper Joseph Sanchez Everett Espinoza Anthony Baros

  2. Video Gaming: A Burgeoning Industry • As of 2002, Video Gaming was a $30 Billion per year industry. • Game sales rival CD, DVD, and Box Office sales. • Example: Sony Playstation 2 • 25 million consoles sold, 2.6 million connected (with separate adapter).

  3. Online Gaming: The Next Big Thing • SCEA study has noted that the peak time for online play is between 5 and 11 pm • Nielsen (Television ratings) has noted a drop in the number of viewers among males 18-24 during the same time period. • The rise of online gaming and video gaming has had a huge impact on entertainment landscape.

  4. Previous Work • Many previous studies have been done to measure Web or Multimedia traffic • Game traffic should be different: Low latency, small packet size, bursty traffic

  5. A Challenge to the Existing Infrastructure of the Internet • Large amount of users requires a relatively high bandwidth, massively scalable method of connecting to servers. • How do gaming developers and system administrators meet this need?

  6. Profiling Traffic from Online Gaming Three focuses for our project: • What type of traffic is used to connect between clients and servers? • How many gamers are connecting, and how does the number change throughout the day? • What is the geographic distribution of online gamers?

  7. Possible benefits of this study • Good data will allow the design of networks to better accommodate network gaming traffic “turbulence” • Provide data for more realistic network simulations

  8. Type of Traffic • Hypothesis: mostly UDP, with TCP connection to tell server when clients join and disconnect. • Tested using network connection monitoring tool while observing an online game.

  9. Number of Gamers • Hypothesis: Absolute number unknown, but peak should be between 5 and 11 pm, as per the SCEA study. • Test by looking at the number of gamers on “closest” servers at various times during the day.

  10. Geographic Distribution • Hypothesis: Should see a normal distribution on a map of the Southwestern USA. • Means should be at cities (Albuquerque, Phoenix, San Antonio, Denver, etc.) • Test by taking IPs of servers with low lag, then plug those IPs into a location finder.

  11. Test Procedures and Results:

  12. Initially set up server, but no one connected. • Initially set up server, but no one connected. Took different approach: • Looked at servers with latency < 50, and servers located in southwest • Assumed these would be servers connecting to us.

  13. STEAM Screenshot

  14. Steam Screenshot with Server

  15. Testing results for span of two weeks for • Number of Players • Number of Active servers • Types of network connections occuring during gaming • Locations of various servers

  16. Results for individual days of the week: • Number of players • Number of active servers

  17. Mechanism for determining types of gaming connections: Used Netmon 1.1, which records the following: • Remote Address • Local address • Port number used • Type of protocol used

  18. NETMON Screenshot

  19. This software allowed us to determine the various types of connections utilized throughout game play

  20. Sample of data file created by Netmon:

  21. Types of connections determined: • There are TCP Connections to central Steam server. These connections perform basic control functions • Majority connection types are UDP, which contain the bulk of the data. UDP connection is the basic server-client connection.

  22. Geographic Distribution was used to find out the organization which a IP address belonged to and thus the location of the server’s city

  23. Sample Data: OrgName: Game Daemons, Inc. OrgID: GAMED Address: P.O. Box 702 City: Midway StateProv: UT PostalCode: 84909 Country: US NetRange: - OrgName: Cyberbasin Internet Services OrgID: CIS-218 Address: 407 N Big Spring City: Midland StateProv: TX PostalCode: 79702 Country: US NetRange: -

  24. After looking up the location of 50 IP addresses, this is the distribution:

  25. Conclusions • Network games different than typical Internet applications • Very Interactive • Traffic flow profile consists mainly of small bursts of UDP packets • For Counter-strike game, we see burstier data rate depending upon action • Packet sizes for server depend upon action