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DVBMN - l : D elay V ariation B ounded M ulticast N etwork with mu l tiple paths

DVBMN - l : D elay V ariation B ounded M ulticast N etwork with mu l tiple paths. Abhishek Bhattacharya & Zhenyu Yang. Roadmap. Introduction Motivation Proposed Heuristic Simulation Results Conclusion. Introduction.

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DVBMN - l : D elay V ariation B ounded M ulticast N etwork with mu l tiple paths

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  1. DVBMN-l: Delay Variation Bounded Multicast Network with multiple paths Abhishek Bhattacharya & Zhenyu Yang

  2. Roadmap • Introduction • Motivation • Proposed Heuristic • Simulation Results • Conclusion

  3. Introduction • Distributed multi-party/multi-stream systems such as 3D Tele-immersion, Networked Virtual Environments and Multi-Player Online games are becoming popular • Multiple streams are streamed to different sites through an overlay network and application-level multicasting • Synchronization among the different streams is crucial for real-time and collaborative interaction • QoS guarantees such as end-to-end delay and delay variation to be satisfied

  4. Motivation • Source : VS • Multicast Set (M): V2, V5, V8 • End-to-End Delay(∆) refers to the delay between the source and the destination node • Goal is to select paths so that VS,V2, V5 and V8 are connected • If we only consider only ∆ then the multicasting solution is: • Vs  V1  V2 (31) • Vs  V7  V8  V5 (26) • Vs  V7  V8 (20)

  5. Motivation • Consider delay variation which is the max variation among the paths from VS to V2, V5 and V8 to minimize: max (|D (s, i) – D (s, j)|) • Optimal result: Vs  V7  V8  V5  V4  V2 (40) Vs  V1  V2  V5 (40) Vs  V1  V2  V4  V8 (43) • Delay Variation = 3 (optimal) • This is the case when l = 1 • We consider delay variation for multiple paths: 1<= l <= k where k is satisfied by all path values less than Δ

  6. Motivation • When l > 1 then there are 2 more constraints to be considered: • Inter-destination delay variation: delay variation between path delay values to V2, V5 and V8 • Intra-destination delay variation: delay variation between path delay values among k multiple paths of V2 or V5 or V8 • We consider the combined inter-intra destination delay variation among k-paths from VSto V2, V5 and V8 • For l = 2 the solution will be: Vs  V7  V8  V5  V2 (35) Vs  V7  V8  V5  V4  V2 (40) Vs  V1  V2  V5 (40) Vs  V1  V2  V4  V5 (45) Vs  V1  V2  V4  V8 (43) Vs  V1  V2  V5  V8 (46) Inter-intra delay variation = 11

  7. Proposed Heuristic • A two-step framework • First stage involves using an efficient k-shortest path algorithm from the literature which returns a path list for every vЄM • All the entries from the above path list satisfy source-to-end maximum delay bound and are sorted in ascending order • The second step involves selecting those optimal paths for each destination node such that the max inter-intra delay variation among those paths have the tightest possible bounds

  8. Proposed Heuristic For k = 4 and Δ = 55 the k-shortest path algorithm will return: V2: 31, 32, 35, 40 V5: 26, 32, 40, 45 V8: 20, 43, 46, 52

  9. Proposed Heuristic • Initially we extract l elements from each of the three lists and insert then into a heap • In each iteration, we calculate the delay variation and update if lesser than the previous one • Then, we extract the least element from the heap and insert the next element from same destination list into the heap • This process will exhaustively search all the possible combinations and at the end will keep the best possible combination of path delay values • Time Complexity: O(mk log m)

  10. Proposed Heuristic • The execution path of the algorithm will be as follows: • V2: 31, 32, 35, 40 • V5: 26, 32, 40, 45 • V8: 20, 43, 46, 52 • Iteration 1: 20, 26, 31, 32, 32, 43 <23> • Iteration 2: 26, 31, 32, 32, 43, 46 <20> • Iteration 3: 31, 32, 32, 40, 43, 46 <15> • Iteration 4: 32, 32, 35, 40, 43, 46 <14> • Iteration 5: 32, 35, 40, 40, 43, 46 <14> • Iteration 6: 35, 40, 40, 43, 45, 46 <11> • Optimal solution is: 35, 40, 40, 43, 45, 46 <11>

  11. Simulation Results • We performed experiments comparing the execution times of Chains Proposed by Banik et. al. • Result shows notable performance gains of our approach compared to Chains • Our approach is also scalable since the execution time grows very slowly with the increase in the number of destination nodes unlike Chains which has a sharp rise

  12. Simulation Results

  13. Conclusion • We investigated the multi-stream synchronization problem for 3DTI and other collaborative applications which require strict QoS guarantees such as delay variation bounds with real-time requirements • The solution is proposed in a two-step framework consisting of finding the k-shortest path and then the optimal path list search algorithms • Our solution satisfies the tightest inter-intra destination delay variation bound along with the end-to-end delay bound for multiple paths • Time complexity of our solution is O(mk log m)which is better than Chains {O(m2k) } even for l = 1 • Future work involves considering the synchronization problem with multiple sources, embedding this component into the current 3DTI architecture and various issues related to network and link dynamics

  14. Thank You........ Questions ???

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