1. 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

2. Outline ● The goal of the paper ● About the game ● About the trace ● About the method of the research ● The analysis of the results ● Implication ● Conclusion

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

4. Why games? ● 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).

5. Why FPS? ● Gaming traffic dominated by first-person shooter games (FPS).

6. Why CS ? ● 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.

7. About the game... ● 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

8. About the game... ● 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

9. The trace – the 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

10. The trace – Summarizes of the trace

11. The trace – total bandwidth Figure 1 (a) total bandwidth obeserved at the server ● Aggregate bandwidth - around 900 kbps

12. The trace – total bandwidth Figure 1(a)Total bandwidth

13. The trace – packed load Figure 1 (b) packed load observed at the server ● packet rate - around 800 pps

14. The trace – packed load Figure 1 (b) packet load

15. About the hurst parameter ● 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

16. Variance time plot Normalized to base interval of 10ms Figure 2

17. The analysis of the results - behavior at varying time scales Figure 3 (a)Interval size=10ms Figure 3 (b)Interval size=50ms ● 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

18. The analysis of the results - 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=1secFigure 3 (d)Interval size=30min

19. The analysis of the results - Finding 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

20. The analysis of the results - Packet 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.

21. Implications ● 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.

22. Implications ● 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.

23. Conclusion ● 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.

24. Questions?