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  • The incredible simultaneous online users reveals the outstanding scalability of the data-driven protocol
  • Previous empirical study has shown that “rarest-first” is one of the most efficient strategies in data dissemination A block that is in danger of being delayed beyond the deadline should be with more priority than the one that is just entering the exchanging window
  • Transcript

    • 1. On the Optimal Scheduling for Media Streaming in Data-driven Overlay Networks Meng ZHANG with Yongqiang XIONG, Qian ZHANG, Shiqiang YANG Globecom 2006
    • 2. Outline
      • Background
      • Related Work
      • Problem Statement and Formulation
      • Global Optimal Solution
      • Distributed Algorithm
      • Performance Evaluation
      • Conclusion & Future Work
    • 3. Background
      • The Internet has witnessed a rapid growth in deployment of data-driven (swarming based) overlay/peer-to-peer network based IPTV systems during recent years.
      • These products are based on data-driven protocol
      • Facts of concurrent online users
        • GridMedia: over 230,000 , rate 310kbps (achieved by one server) (developed by our lab)
        • PPLive: 500,000 , rate 300-500kbps
        • QQLive: 1,460,000 , rate 300-500kbps (not one server)
    • 4. Background - Data-Driven Protocol Review
      • Aiming to enable large-scale live broadcasting in the Internet environment
      • Very simple and very similar to that of Bit-Torrent
      • Two steps in data-driven protocol
        • The overlay construction
        • The block scheduling
    • 5. Background - Data-Driven Protocol Review
      • The first step – overlay construction
        • All the nodes self-organize into a random graph
      I have block 1,2,4 I have block 1,2,3 I have block 1,2 I have block 2,3 Request block 4 Request block 3 Request block 1 Request block 2 Send block 4 Send block 3 Send block 1 Send block 2
      • The second step – block scheduling
        • The streaming is divided into blocks
        • Each node has a sliding window containing all the blocks it is interested in currently
    • 6. Related Work
      • To improve data-driven protocol, most recent efforts focus on optimizing overlay construction (i.e. the first step ):
        • Vishnumurthy & Francis (INFOCOM2006): random graph building under heterogeneous overlay
        • Liang & Nahrstedt (INFOCOM2006): propose RandPeer, a peer-to-peer QoS-sensitive membership management protocol
    • 7. Related Work
      • An problem not well addressed is how to optimize the second step, that is,
        • how to do optimal block scheduling and maximize the throughput of data-driven protocol under a constructed overlay
      • Most existent methods are straight forward and ad hoc
        • Chainsaw: pure random way
        • DONet: greedy local rarest-first
        • PALS: round-robin method
    • 8. Problem Statement and Formulation
      • How to do optimal scheduling to maximize the throughput of the whole overlay?
      • The real situation is more complicated because different blocks may have different importance and the bottlenecks are not only at the last mile.
      • Our basic approach:
        • Define priority to different blocks due to their importance
        • Maximize the sum of priorities of all requested blocks
      Throughput is 4 Optimal scheduling, throughput gain is 25% Some requests congestion at node 1 Local Rarest First (LRF) strategy
    • 9. Problem Statement and Formulation - Priority Definition
      • We use two factors to represent the significance of a block:
        • rarity factor
        • emergency factor
      • We define the priority of block j ∈ A i for node i ∈ R as follow:
        • P j i = βP R ( Σ k ∈Nbr( i ) h kj )+(1- β ) P E ( C i + W T - d j i ),
        • Where 0≤ β ≤1 , functions P R ( * ) (rarity factor) and P E ( * ) (emergency factor) are both monotonously non-increasing ones
    • 10. Problem Statement and Formulation - Formulation
      • Decision variable
      • Global block scheduling problem:
      • s.t.
      set of all absent blocks in the current exchanging window of node i D i play out time of block j at node i d j i the current play out time of node i C i the exchanging windows size W T period of requesting new blocks τ set of neighbors of node i NBR i Blocks availability: “ a kj =1” denotes node k holds block j ; otherwise, “ a kj =0” h kj ∈{0,1} the end-to-end available bandwidth between node i and node k E ik , the outbound bandwidth of node i O i , the inbound bandwidth of node i I i , N +1 is the number of overlay nodes, where node 0 is the source node N Definition Notation
    • 11. Global Optimal Solution
      • Convert the global block scheduling formulation into an equivalent Min-Cost Flow Problem
    • 12. Global Optimal Algorithm
      • Proposition:
        • The optimal goal of global block scheduling problem has the same absolute value as the minimum flow amount of its corresponding min-cost network flow problem. The flow amount on arc (v ki n , v ij b ) ∈{0, 1} is just the value of x kj i , which is the solution to the optimal block scheduling.
      • Algorithm complexity:
        • O ( nm (loglog U )log( nC )) , where n and m are the number of vertices and arcs while U and C is the largest magnitude of arc capacity and cost
    • 13. Distributed Algorithm
      • We first use a simple way to estimate the bandwidth that is available from each neighbor with historical information.
      • q ki ( m ) : the total number of blocks arrived at node i from neighbor k in the m th period.
      • W ki ( m +1) : the estimated bandwidth from node k to node i
    • 14. Distributed Algorithm
      • With the estimated available bandwidth, a local block scheduling is performed on each node
      • It can be also transformed into an equivalent min-cost network flow problem for local optimal request
    • 15. Distributed Algorithm
      • Heuristic distributed algorithm:
        • Node i estimates the bandwidth W ki ( m +1) that its neighbor k can allocate it in the ( m +1) th period with the traffic received from that neighbor in the previous M periods, as shown in equation (3);
        • Based on W ki ( m +1) , node i performs the local block scheduling (2) using min-cost network flow model. The results x kj i ∈{0,1} represent whether node i should request block j from neighbor k ;
        • Send requests to every neighbor.
    • 16. Performance Evaluation - Compared Scheduling Methods
      • Random Strategy: each node will assign each desired block randomly to a neighbor which holds that block. Chainsaw uses this simple strategy.
      • Local Rarest First (LRF) Strategy: A block that has the minimum owners among the neighbors will be requested first. DONet adopts this strategy.
      • Round Robin (RR) Strategy: All the desired blocks will be assigned to one neighbor in a prescribed order in a round-robin way. If there is multiple available senders, it is assigned to a sender that has the maximum surplus available bandwidth.
    • 17. Simulation Configuration
      • For a fair comparison, all the experiments use the same simple algorithm for overlay construction
      • Delivery ratio : to represent the number of blocks that arrive at each node before playback deadline over the total number of blocks encoded.
      • DSL nodes:
        • Download bandwidth: 40% 512K, 30% 1M, 30% 2M
        • Upload bandwidth: half of download bandwidth
      • 500 nodes
      • Each node has 15 neighbors
      • Request period: 2 second
    • 18. Simulation Results
      • All are DSL nodes with exchanging window of 10 sec and bottlenecks only at the last mile. Group size is 500
    • 19. Simulation Results
      • All are DSL users with exchanging window of 10 sec and end-to-end available bandwidth 10~150Kbps. Group size is 500
    • 20. Conclusion & Future Work
      • The contributions of this paper are twofold.
        • First, to the best of our knowledge, we are the first to theoretically address the streaming scheduling problem in data-driven (swarming based) streaming protocol.
        • Second, we give the optimal scheduling algorithm under different bandwidth constraints, as well as a distributed asynchronous algorithm which can be practically applied in real system and outperforms existent methods by about 10%~80%
      • Future work
        • How to do optimization over a horizon of several periods, taking into account the inter-dependence between the periods.
        • How to do optimal scheduling with scalable video coding (such as layered video coding) or multiple description coding
    • 21.
      • Thanks
      • Q&A

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