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Multi-Source Latency Variation Synchronization for Collaborative Applications

Multi-Source Latency Variation Synchronization for Collaborative Applications. Abhishek Bhattacharya, Zhenyu Yang & Deng Pan. Roadmap. Introduction Problem Motivation Heuristic Solution Results Summary. Introduction.

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Multi-Source Latency Variation Synchronization for Collaborative Applications

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  1. Multi-Source Latency Variation Synchronization for Collaborative Applications Abhishek Bhattacharya, Zhenyu Yang & Deng Pan

  2. Roadmap • Introduction • Problem • Motivation • Heuristic Solution • Results • Summary

  3. Introduction • Single-source/Single-stream Multicast Network: Construction of a MST connecting a single source and multiple receiver nodes • Single-source/Multi-stream Multicast Network: Content distribution systems using MDC/SVC streams. • Multi-source/Single-stream Multicast: Constructing a forest of trees connecting multiple sources with multiple receiver nodes. • Multi-source/Multi-stream systems such as 3D Virtual Immersive Systems, 3D Videoconferencing, Online games, etc.

  4. Example – Multi-source/Multi-stream System • 3DTI environment provides a collaborative virtual space for geographically distributed users. • Multiple 3D cameras installed for capturing the same scene from various viewpoints. • Each 3D camera produces a video stream. • Multiple streams are transmitted from each node to all other nodes since each node acts a source and a receiver.

  5. Node B S1AB S7BA S2AB Node A S6BA S4BC S5BC S3AC S1CB S8CB S3AC Node C S7CA 3D Camera S6CA Display Unit Skij camera stream ‘k’ from node ‘i’ to node ‘j’

  6. Introduction • Latency Variation is an important QoS constraint for Multi-source/Multi-stream systems. • Problem known as Delay Variation Bounded Multicast Network (DVBMN) in the literature. Proved to be NP-complete by Rouskas et al. and thereafter many heuristics proposed. • Multi-source/Multi-stream latency and latency-variation constraints: E2E from source to destination, Inter-stream, Inter-Source & Intra-Source Latency Variation. • Intra-source is important for 3DTI applications due to the high correlation factor among the multiple streams from the same source.

  7. Node B S1AB S7BA S2AB Node A S6BA S4BC S5BC S3AC S1CB S8CB S3AC Node C S7CA S6CA Intra-SourceVariation Inter-Source Variation

  8. Problem P: E2E Latency from Source to Destination: C1: Inter-Stream Latency Variation: C2: Inter-Source Latency Variation: C3:

  9. ∆ = 22 Motivation S16:V1 V4  V6 : 12 S17:V1 V4  V7 : 16 S26 : V2 V4  V6 : 16 S27 : V2 V5  V7 : 12 V1 V3  V4  V6 : 17 V1 V3  V4  V7 : 21 V2 V1  V4  V6 : 17 V2 V4  V7 : 20 V3 12 V6 6 9 7 4 V7 V1 8 8 β = 16 – 12 = 4 β = 21 – 17 = 4 V4 2 5 λ6 = 16 – 12 = 4 ; λ7 = 16 – 12 = 4 λ6 = 17 – 17 = 0 ; λ7 = 21 – 20 = 1 12 V5 10 V2 λT = Max(λ6 , λ7) = 4 λT = Max(λ6 , λ7) = 1

  10. Motivation

  11. Heuristic Solution: Algorithm • A 2-step framework: • K-shortest-path Algorithm: •  Involves the computation of k-shortest path from each • source to all the destination nodes. •  Generates ‘d*s’ lists with ‘k’ elements in each list. •  State-of-art ksp Algorithm: Recursive Enumeration • Algorithm(REA) by Jimenez et al.

  12. Heuristic Solution: Algorithm • M SLV Algorithm: •  Involve a selection of paths for the construction of forest • i.e., multiple multicast trees connecting one source node • to the set of destination nodes. •  Selecting ‘d*s’ path latency values from ‘d*s*k’ elements. • Path Latency values. • The Path Latency values should satisfy the constraints: • C1: E2E latency bound (∆), C2: Latency Variation among • any path(β), and C3: Inter-Source LatencyVariation(λT).

  13. Heuristic Solution: Algorithm T = 4; λ6 = 4; λ7 = 4; λT = 4 S16 12 12 12 17 17 17 18 18 T = 5; λ6 = 1; λ7 = 4; λT = 4 T = 4; λ6 = 1; λ7 = 4; λT = 4 S26 16 16 16 17 17 17 21 21 T = 4; λ6 = 1; λ7 = 3; λT = 3 T = 3; λ6 = 0; λ7 = 3; λT = 3 S17 16 16 16 17 17 17 21 21 T = 4; λ6 = 0; λ7 = 1; λT = 1 T = 4; λ6 = 4; λ7 = 1; λT = 4 S27 12 12 12 20 20 24 T = 3; λ6 = 3; λ7 = 1; λT = 3 ∆ = 25 ; β = 4

  14. Heuristic Solution: Time Complexity • ksp-Algorithm: O(m + nk * log(m/n)) • m number of total edges in the network graph • n  number of total nodes in the network graph • k  number of shortest paths • MSLV Algorithm: O(dsk * log(ds)) • d  number of destination nodes in the multicast • set • s  number of source nodes in the multicast set

  15. Results Detailed results in the paper

  16. Summary • Studied construction of multicast networks with multiple sources • and receivers. • Satisfying different Latency Variation constraints which are • important for real-time multi-party/multi-stream systems. • A 2-step heuristic framework consisting of an initial ksp-algorithm • to generate shortest paths from sources to receivers followed by • a path selection process to satisfy the various hard/soft • constraints. • Future work involves to investigate the problem with link capacities • as time-varying functions and decentralized solutions with • node/network dynamics.

  17. Thank You........ Questions ???

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