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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. - PowerPoint PPT Presentation
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DVBMN-l: Delay Variation Bounded Multicast Network with multiple paths
Abhishek Bhattacharya & Zhenyu Yang
Roadmap
• Introduction
• Motivation
• Proposed Heuristic
• Simulation Results
• Conclusion
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
MotivationSource : 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)
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 <= kwhere k is satisfied by all path values less than Δ
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 VS to 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
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 v Є M
• 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
Proposed Heuristic
For k = 4 and Δ = 55 the k-shortest path algorithm will return:V2: 31, 32, 35, 40V5: 26, 32, 40, 45V8: 20, 43, 46, 52
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)
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>
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
Simulation Results
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
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