1 Efficient Management of Data Center Resources for Massively Multiplayer Online Games V. Nae, A. Iosup, S. Podlipnig, R. Prodan, D. Epema, T. Fahringer, SC’08 Shimin Chen Big Data Reading Group

2 Motivation Massively Multiplayer Online Games (MMOGs)  Popular in the past decade Existing approach:  Per game dedicated multi-server infrastructure E.g., World of Warcraft has > 10,000 servers  Over-provisioning for highly dynamic demands Daunting for new game providers to join the market!

3 This Paper Multiple games share many data centers  in-game monitoring Player interaction important  load prediction Neural network based  dynamic resource allocation  Geographical location of data centers with respect to user locations is taken into account

4 Outline Introduction A model for the MMOG ecosystem MMOG workload analysis Load prediction for MMOGs Resource provisioning and management Conclusion

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6 MMOG Application Model Large-scale simulations of game worlds  Avatars: representation of players  NPCs or bots: non-player characters  Mobiles: other entities that can be interacted with  Decor: immutable entities Client-Server architecture:  Game providers maintain servers  Players run clients that connect to servers  Clients send play actions to servers  Servers compute game world state (positions of entities, interactions, etc.) then send responses to clients Smooth game experience is critical for success  Lack of responsiveness  people leaving the game  lose money

7 Characteristics Vary Greatly Interactions between players span a wide range  Very low: e.g., puzzle games  Low: e.g., RPG (role-playing game), a small group of players interact with a sparse environment  High: e.g., FPS (first-person shooter game), players are fighting against each other in a confined area The algorithms can there be O(n) – O(n 3 )  n: number of entities An common optimization is to only compute area of interest of each avatar

8 Data Center Model Many data centers scattered around the world Four types of resources:  CPU time, memory, ExtNetin, ExtNetout  (disk storage is not important?) Game providers submit resource requests Data centers allocate resources Each data center may enforce a particular size granularity for allocations (called space-time policy)  Requests are rounded up to this

9 Ecosystem Multiple data centers  Each may host multiple games Multiple game providers  Each may provide multiple games Each game may run on multiple data centers Resource allocation goals:  Allocated resources must match or larger than required  Locate resources closest to users  Select as finer-grain recourses with shorter reservation times as possible

10 Outline Introduction A model for the MMOG ecosystem MMOG workload analysis Load prediction for MMOGs Resource provisioning and management Conclusion

11 RuneScape Traces Ranked #2 by number of players in US and EU  5 million active players, 8 million open accounts  Combine elements of RPG and FPS  Game load cannot be trivially computed Trace: Aug 2007 – July 2008  Collected from the official RuneScape web page  Number of players over time for each server group  Record per two minutes

12 Number of concurrent players change greatly  over-provisioning

13 Region 0 (Europe) Loads 40 server groups, 2-week trace, sample / 2 minutes Strong diurnal pattern, but no weekend effects

14 Influence of Player Interaction on Load Tcpdump for 8 game sessions at clients

15 Inter-Arrival Time Large differences

16 Outline Introduction A model for the MMOG ecosystem MMOG workload analysis Load prediction for MMOGs Resource provisioning and management Conclusion

17 Two Options Explanatory models:  Tightly coupled with applications and platform  Difficult to obtain and update for dynamic complex systems Time series prediction  Neural network (another paper from the group)  Task: predicting entity counts of each sub-zone

18 Methodology MMOG Emulator:  Modeling different type of players  (No validation?) Test algorithms on the trace from the emulator

19 Prediction Results No sure if the time vs. prediction tradeoff favors the approach here?

20 Outline Introduction A model for the MMOG ecosystem MMOG workload analysis Load prediction for MMOGs Resource provisioning and management Conclusion

21 Experimental Setup Evaluation space:  Resource allocation mechanisms: static, dynamic  Prediction algorithms  Player interaction/model updates complexity  Hosting policies  Latency tolerance  Number of MMOGs Use RuneScape trace to model number of players and their distribution

22 Data Centers Emulation on real machines? Simulation? Not sure

23 Impact of Prediction Performance

24 Static vs. Dynamic Allocation Allocation granularity 6 hours

25 Impact of Player Interaction

26 Multiple MMOGs A: O(nlog(n)); B: O(n 2 ); C: O(n 2 log(n)) Clearly, the efficiency of the provisioning system is determined by the biggest consumer!

27 Conclusion Multiple MMOGs, multiple data centers Workload analysis Applying workload prediction to provisioning Emulation/simulation study  Not very convincing Ongoing work: