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Multi-Source Latency Variation Synchronization for Collaborative Applications

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17 diapositives · 306 Views · Sep 14, 2014

This paper addresses the challenge of latency variation in multi-source, multi-stream collaborative applications. It outlines a comprehensive roadmap including problem motivation, heuristic solutions, and results from heuristic algorithms designed to meet Quality of Service (QoS) constraints. The study specifically focuses on approaches to construct multicast networks involving multiple sources and receivers, ensuring efficient content delivery in systems like 3D videoconferencing and online games. Detailed algorithms, including K-shortest-path and M-SLV, are explored to address the NP-complete nature of the Delay Variation Bounded Multicast Network (DVBMN) problem.

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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 ???