Multicast Tree Reconfiguration in Distributed Interactive Applications

1. Multicast Tree Reconfiguration in DistributedInteractive Applications Pål Halvorsen1,2, Knut-Helge Vik1 and Carsten Griwodz1,2 1Department of Informatics, University of Oslo, Norway 2Simula Research Laboratory, Norway

2. Game environment Real-World Proximity Virtual World Proximity • Typical massive multiplayer online games today • Central server-based • Experience high latency • Physical world and virtual world locality are unrelated

3. Game environment • Example: 1 hour trace of one region of Anarchy Online • Here: Most action in Europe • At other times of day the center of action shifts • Average latency could be reduced considerably Asia Europe North America

4. For average area of interest: find a node that improves average latency Let this “leader” node handle state on behalf of the server Variation of the central server approach Remaining problem: Network utilization This leader node is probably less powerful than the server Games traffic is not adaptive Reducing the worst-case latency S L Transfer state

5. Non-adaptive games traffic • Games traffic: UDP or TCP • UDP is not adaptive • TCP games traffic is not either !!! Number of packetsper round-trip time • Games connections are so thin that TCP’s congestion control does not apply • We should conserve network resources

6. Tree structure saves resources Alleviates the communication overhead Best results: Minimum Spanning Tree (MST) or Steiner Minimum Tree (SMT) Tree computation necessary for each join and leave MST and SMT computations are Too slow Usually centralized Need fast heuristics Reducing tree cost S L Transfer state Lowerworst-casedelay L n New node enters

7. Various Join operations all LEAVE operations: remain in the graph until degree is 2 or less

8. Effects of Join operations • Tested on several topologies generated using BRITE • Here: several iterations of groups with Zipf-distributed popularities Time for entering a group Sum of all edge delays • When groups are small • complex algorithms are faster than simple ones • complex algorithms provide better results

9. Effects of Join operations • Tested on several topologies generated using BRITE • Here: several iterations of groups with Zipf-distributed popularities Time for entering a group Sum of all edge delays • When groups are large and constantly changing • “connect best” takes too long without any performance gain • cost of more complex algorithms decreases quickly

10. Effects of Join operations • Tested on several topologies generated using BRITE • Here: several iterations of groups with Zipf-distributed popularities Time for entering a group Sum of all edge delays • When groups are very large and constantly changing • nearly all nodes remain in the graph because we • do allow nodes to leave only when they 2 or fewer neighbors

11. Conclusions • Join operation by itself is not sufficient to define a tree • Fast join operation is preferable for small groups • Cost-conscious join operations are preferable for large groups • Currently investigating Minimum Spanning Tree and Steiner Tree Heuristics • Goal is to evaluate some that areDistributed and Dynamic • Compare then with simple Join approaches • Future work • Resilience through pre-defined backup paths