1. Locality Aware Dynamic Load Management for Massively Multiplayer Games Jin Chen, Baohua Wu, Margaret Delap, Bjorn Knutsson, Margaret Delap, Bjorn Knutsson, Honghui Lu, Cristiana Amza University of Toronto & University of Pennsylvania

2. Massively Multiplayer Online Games (MMOGs) A large number of players (e.g., 100K) SLA: Update interval to players (e.g., 1 sec) Game hosted on distributed servers

3. State-of-art game world partitioning Ad hoc partitions Countries, rooms

4. The scalability problem Flocking Players move to one area or hotspot Overload the server hosting the hotspot Static partitioning is impractical! Static partitioning is impractical!

5. Current solution Admission control Gateways E.g., airports, doors

6. Our solution: Contiguous world Seamless partition Players can “see” across server boundaries Players can smoothly transfer Inter server communication Boundary information Player handoff

7. Dynamic load management Regions Granularity for load management Region remapping Migrate regions from one server to another

8. Locality aware load management Keep adjacent regions on same server Less locality, higher inter-server communication Global reshuffle Block partition

9. Roadmap The algorithm ImplementationResults

10. Goals of a locality aware load management algorithm Balancing the server load in terms of the numbers of players Minimizing inter-server communication Goals conflict ! Desirable: Low number of region remappings Avoid thrashing

11. Our algorithm Load shedding algorithm Goal: Balance load & maintain locality Triggered by upper limit of load Locality aware aggregation algorithm Goal: Correct locality disruption Triggered by high inter-server communication in normal load

12. Load shedding algorithm Triggered by load >= upper limit Target: safe load First priority: Load shedding to neighbor servers Balance load & preserve locality If neighbors are also overloaded, load shedding to lightly loaded servers

13. Load shedding Quest!

14. Load shedding under quest

15. Load shedding to neighbors

16. Load shedding to lightly loaded servers Added more inter-server communication!

17. Locality aware aggregation Triggered by SLA violations & load is normal Aggregation Merge boundary regions into neighbor partitions

18. Locality aware aggregation (1)

19. Locality aware aggregation (2)

20. Graph model Local load graph for each server The partition of game map on a server A vertex  a region Vertex weight  load An edge  adjacent relationship of two regions

21. A game map hosted by a server

22. 30 23 75 60 70 68 70 9278 A local load graph

23. Heuristic graph partitioning Goal Preserve locality & meet weight constraints 2375 60 70 68 30 68709278

24. Locality metric: Number of connected components

26. Experiment methodology Single server experiments with Simmud A MMOG game server from UPenn Bottleneck is bandwidth Determine algorithm parameters Collect traces Simulate a large scale game

27. Simulation setting 100 servers and 400 regions 6000 independent players Traces collected from SimMud A LAN cluster server system 100 Mbps inter-server bandwidth 100 Mbps server-players bandwidth A WAN distributed server system 100 Mbps shared network bandwidth towards both its players and neighbor servers SLA: 2 seconds

28. Algorithms used for comparison Static partitioning Block partitioning Spread Global reshuffle Lightest Shed load to a lightly loaded server Locality

29. A LAN cluster server system Quest lasts during 0-1000 sec

30. A WAN distributed server system Quest lasts during 0-1000 sec

31. A WAN distributed server system MetricLocalityLightestSpread # of Region Remap. 452988058 # of Connected Components 104116399

32. Conclusion Presented a load management algorithm Supports seamless game world partitioning Uses a locality aware algorithm Achieves better performance (SLA) Up to a factor of 8 compared to Static Up to a factor of 6 compared to global reshuffle

33. Thanks!