1 / 16

Research: Group communication in distributed interactive applications Student: Knut-Helge Vik

Research: Group communication in distributed interactive applications Student: Knut-Helge Vik Institute: University of Oslo, Simula Research Labs. Outline. MiSMoSS Motivation Group Communication Application Layer Multicast Tree algorithms Research Conclusions. Real-World Proximity.

pilar
Download Presentation

Research: Group communication in distributed interactive applications Student: Knut-Helge Vik

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Research: Group communication in distributed interactive applications Student: Knut-Helge Vik Institute: University of Oslo, Simula Research Labs

  2. Outline • MiSMoSS • Motivation • Group Communication • Application Layer Multicast • Tree algorithms • Research • Conclusions

  3. Real-World Proximity Virtual World Proximity MiSMoSS Project • Investigate Large-scale interactive applications • Main issue: Latency • Three sub-projects: • Latency hiding • prediction • Group communication management • Overlay multicast • Transmission protocol optimization • Thin streams

  4. Large-scale interactive applications Users interact in groups Communication demands vary within an application low latency demands high bandwidth demands frequent group membership changes consistency Build overlay routing with a small diameter with degree limitations using algorithms with low execution times with stable reconfigurations Motivation - Group communication management

  5. Motivation • Example application: Massively Multiplayer Online Games • Large scale (thousands of simultaneous users) • Central server-based (experience high latency) • Clients far apart in physical world, but near in virtual world • Issues: Event distribution • Goal: Reduce latency, decrease server load, increase MMOG size • Group Communication – Application Layer Multicast • Overlay Multicast – Must handle group dynamics • Current overlay multicast protocols lack efficient dynamic handling • Goal: Create a dynamic overlay multicast protocol Real-World Proximity Virtual World Proximity

  6. Research - Summary • Group membership - join/leave • Insert or remove group members to an existing topology • Overlay multicast – fully meshed graph: • Optimization techniques – edge pruning, core selection • Multicast trees • Investigating tree problems: • Shortest path tree • Minimum spanning tree • Steiner minimum tree – SPH, DNH, ADH • Minimum diameter degree limited spanning tree • Dynamic tree algorithms – insert and remove • Tree algorithm constraints: • unconstrained • degree and/or delay constrained • Metrics: • Stress - degree • Diameter – maximum pairwise latency • Total tree cost – sum of edge weights • Reconfiguration time – time it takes to complete reconfiguration • Edge change – number of link changes in a reconfiguration

  7. Research - Optimization • Application layer graphs are fully meshed • Ex: |V|=1000, |E| = 499500 edges, |E_T|= |V| - 1 (using 0.02 % of the edges) • Tree algorithms build trees using graphs • Graph optimization techniques • Edge pruning algorithms: k-Best links • Limit nodes to group members – steiner minimum trees? • Core selection heuristics: • Include stronger nodes in the input graph – higher stress capacity • Especially suitable for SMT heuristics • Group center, topological center, MDDL center • Goal: Reduce reconfigure time while preserving tree quality

  8. Research - Group Dynamics • Dynamic membership – nodes join and leave the multicast tree dynamically • Must insert and remove nodes online • Needs algorithm to reconfigure the tree • Contradictory goals: • Low reconfigure time • efficient tree • tree stability

  9. Research – Reconfiguration Set • Reconfiguration set – nodes involved in reconfiguration • Entire group: • Pros: Tree efficiency • Cons: High reconfiguration time, tree stability • Reduced size of reconfiguration set • Pros: Low reconfiguration time, increased stability • Cons: Reduced tree efficiency

  10. Research – Reconfiguration Set • Reconfiguration set – nodes involved in reconfiguration • Entire group: • Pros: Tree efficiency • Cons: High reconfiguration time, tree stability • Reduced size of reconfiguration set • Pros: Low reconfiguration time, increased stability • Cons: Reduced tree efficiency

  11. pruned Tree Algorithms • Tree algorithms – reconfigures entire tree • Problems in P: Minimum spanning tree (MST), Shortest path tree (SPT) • Problems in NP: Steiner minimum tree (SMT), Minimum diameter degree limited tree, Degree constrained MST, SPT, SMT • Main issues: reconfiguration time is high and tree stability suffers • Heuristics are especially slow • Addressing issues: Reduce number of edges in input graph, include strong cores • Pros: Reduced reconfiguration time, increased stability • Cons: Tree efficiency is also reduced Reconfiguration time Total tree cost

  12. Edge changes MDDBST MDDBST SPH SPH Worst case insert Dynamic Algorithms • Dynamic Algorithms – insert/remove (reconfigure smaller parts of a tree) • basic edge optimization goals: Minimum cost edge, Minimum diameter edge, Minimum cost to source • Prune non member nodes • Main issues: tree efficiency suffers • Always local optimizations • Crowded with non member nodes • Addressing issues: Vary reconfiguration set size, prune non-members, switch non members to stronger cores • Cons: Increased reconfiguration time, reduced stability • Pros: Tree efficiency Edge changes – remove algorithms (100 nodes)

  13. Insert Algorithms • Basic insertion choices – • Insert as leaf – no edge change • Insert and reconfigure – increased tree efficiency but reconfiguration time! • Implemented a number of insert algorithms – ex: • I-MC : insert minimum cost edge • I-MDDL : insert minimum diameter degree limited edge Node is joining Connect to tree as leaf Insert strong core Use as intersection Three configuration examples

  14. Remove Algorithm • Basic remove choices – • Remove leaf – no edge changes (easy) • Remove non-leaf – MUST reconfigure • reconfigure and add/remove non-MN • Implemented a number of algorithms – ex: • RTR-MC – neighbors • RTR-P – pruning non members Node is leaving Keep as non-member Use stronger core Reconnect neighbors Three configuration examples

  15. Remove strategy: RTR-MC 25 25 Remove strategy: RTR-P I-MDDL 20 20 MDDL diameter diameter MDDL 15 15 I-MDDL 10 10 0 20 40 60 80 100 0 20 40 60 80 100 5 5 group size / number of nodes group size / number of nodes Dynamic Algorithms – Insert/Remove reconfigure set leaving reconfigure set RTR-MC RTR-P

  16. Conclusions and Future Work • Current algorithms are centralized • Implement distributed algorithms • PlanetLab implementation • Implement overlay multicast protocol • Investigate mesh vs. trees • Questions?

More Related