Provisioning on line games a traffic analysis of a busy counter strike server
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Provisioning On-line Games: A Traffic Analysis of a Busy Counter-Strike Server. Wu-chang Feng, Francis Chang, Wu-chi Feng, Jonathan Walpole Instructor: Dr. Charles Krasic Presentation by: zhen tan. Outline. The goal of the paper About the game About the trace

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Provisioning on line games a traffic analysis of a busy counter strike server l.jpg
Provisioning On-line Games: A Traffic Analysis of a Busy Counter-Strike Server

Wu-chang Feng, Francis Chang, Wu-chi Feng, Jonathan Walpole

  • Instructor: Dr. Charles Krasic

  • Presentation by: zhen tan


Outline l.jpg
Outline Counter-Strike Server

  • The goal of the paper

  • About the game

  • About the trace

  • About the method of the research

  • The analysis of the results

  • Implication

  • Conclusion


Slide3 l.jpg
Goal Counter-Strike Server

To Understand the resource requirements of a popular on-line first person shooter game


Why games l.jpg
Why games? Counter-Strike Server

  • Because of the Rapidly increasing of games in popularity

    • Forrester Research: 18 million on-line in 2001

    • Consoles on-line

      • Playstation 2 on-line (9/2002)

      • Xbox Live (12/2002).


Why fps l.jpg
Why FPS? Counter-Strike Server

  • Gaming traffic dominated by first-person shooter games (FPS).


Why cs l.jpg
Why CS ? Counter-Strike Server

  • a modification to the popular Half-Life game

  • one of the most popular and most network-intensive first person shooter games played over the Internet.


About the game l.jpg
About the game... Counter-Strike Server

  • In the game Two teams of players competing in rounds lasting several minutes

  • Rounds played on maps that are rotated over time

  • Each server supports up to 32 players


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About the game... Counter-Strike Server

  • Centralized server implementation

    • Clients update server with actions from players

    • Server maintains global information and determines game state

    • Server broadcasts results to each client

  • Sources of network traffic

    • Real-time action and coordinate information

    • Broadcast in-game text messaging

    • Broadcast in-game voice messaging

    • Customized spray images from players

    • Customized sounds and entire maps from server


The trace the server l.jpg
The trace – Counter-Strike Serverthe server

  • cs.mshmro.com (129.95.50.147).

    • Dedicated 1.8GHz Pentium 4 Linux server

    • OC-3

    • 70,000+ unique players (WonIDs) over last 4 months


The trace summarizes of the trace l.jpg
The trace – Counter-Strike ServerSummarizes of the trace


The trace total bandwidth l.jpg
The trace – Counter-Strike Servertotal bandwidth

Figure 1 (a) total bandwidth obeserved at the server

  • Aggregate bandwidth - around 900 kbps


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The trace – Counter-Strike Servertotal bandwidth

Figure 1(a)Total bandwidth


The trace packed load l.jpg
The trace – Counter-Strike Serverpacked load

Figure 1 (b) packed load observed at the server

  • packet rate - around 800 pps


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The trace – Counter-Strike Serverpacked load

Figure 1 (b) packet load


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About the hurst parameter Counter-Strike Server

  • Normalized variance – aggregated sequence divided by the variance of the initial, unaggregated sequence

  • Block sizes –the number of frames per block.

  • Hurst parameter (H) – to measure the variability of the network

  • β – beta is the magnitude of slope of the best fit line through the data points

    The relation of H and β : H=1- 1/2β


Variance time plot l.jpg
Variance time plot Counter-Strike Server

Figure 2

Normalized to base interval of 10ms


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The analysis of the results Counter-Strike Server- behavior at varying time scales

  • Periodic server bursts every 10 ms and 50ms

    • (a)the clients with state updates about every 50ms

    • (b)aggregating over the interval of 50ms smoothes out the packet load

Figure 3 (a)Interval size=10ms

Figure 3 (b)Interval size=50ms


The analysis of the results behavior at varying time scales18 l.jpg
The analysis of the results Counter-Strike Server- behavior at varying time scales

  • Low utilization every 30 minutes

    • (c) Server configured to change maps every 30 minutes

    • (d) increasing the interval size beyond the default map time of 30min

Figure 3(c)Interval size=1sec

Figure 3 (d)Interval size=30min


The analysis of the results finding the source of predictability l.jpg
The analysis of the results - Counter-Strike ServerFinding the source of predictability

  • Games must be fair across all mediums (i.e. 56kers)

    • Aggregate predictability due to “saturation of the narrowest last-mile link”

  • Histogram of average per-session client bandwidth


The analysis of the results packet sizes l.jpg
The analysis of the results - Counter-Strike ServerPacket sizes

  • The outgoing bandwidth exceeds the incoming bandwidth.

  • Rate of incoming packets exceeds that of outgoing packets.

    • Server taking state information from each client.

    • Servers aggregate and broadcast larger global updates.


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Implications Counter-Strike Server

  • Routers, firewalls, etc. must be designed to handle large bursts at millisecond levels.

    • Game requirements do not allow for loss or delay (lag).

      Routing devices that are not designed to handle small packets will see significant packet loss or even worse, when handle game traffic.

    • Should not be provisioned assuming a large average packet size [Partridge98].

      Router designers and vendors often make packet size assumptions when building their gear, often expecting average sizes in between 1000 and 2000 bits (125-250 bytes). Thus, a significant shift in packet size from the deployment of online games will make the route lookup function as the bottleneck of the link speed.


Implications22 l.jpg
Implications Counter-Strike Server

  • Routers, firewalls, etc. must be designed to handle large bursts at millisecond levels.

    • there are buffers anywhere, they must...

      • Use ECN (Explicit Congestion Notification).

      • Be short (i.e. not have a bandwidth-delay product of buffering).

      • Employ an AQM(active queue management) that works with short queues.


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Conclusion Counter-Strike Server

  • The results show that the traffic behavior of this heavily loaded game server is highly predictable and can be attributed to the fact that current game designs target the saturation of the narrowest, last-mile link.

  • As current routers are designed for bulk data transfers with larger packets, a significant, concentrated deployment of online game servers will have the potential for overwhelming current networking equipment.


Questions l.jpg
Questions? Counter-Strike Server


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