On the Optimal Scheduling for Media Streaming in Datadriven Overlay Networks

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On the Optimal Scheduling for Media Streaming in
Data-driven Overlay Networks

• Meng ZHANG
• with Yongqiang XIONG, Qian ZHANG, Shiqiang YANG
• Globecom 2006

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Outline

• Background
• Related Work
• Problem Statement and Formulation
• Global Optimal Solution
• Distributed Algorithm
• Performance Evaluation
• Conclusion Future Work

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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)

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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

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Background - Data-Driven Protocol Review

• The first step overlay construction
• All the nodes self-organize into a random graph

• 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

I have block 1,2,3
Request block 3
Send block 3
I have block 1,2,4
Request block 4
Send block 4
I have block 1,2
I have block 2,3
Request block 1
Request block 2
Send block 1
Send block 2
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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

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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

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Problem Statement and Formulation
Local Rarest First (LRF) strategy
Throughput is 4
Optimal scheduling, throughput gain is 25
Some requests congestion at node 1

• 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

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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?Ai for node i?R
  as follow
• Pji ßPR(Sk?Nbr(i)hkj)(1-ß)PE(CiWT-dji),
• Where 0ß1, functions PR() (rarity factor) and
  PE() (emergency factor) are both monotonously
  non-increasing ones

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Problem Statement and Formulation - Formulation

• Decision variable
• Global block scheduling problem
• s.t.

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Global Optimal Solution

• Convert the global block scheduling formulation
  into an equivalent Min-Cost Flow Problem

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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
  (vkin, vijb) ?0, 1 is just the value of xkji,
  which is the solution to the optimal block
  scheduling.
• Algorithm complexity
• O(nm(loglogU)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

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Distributed Algorithm

• We first use a simple way to estimate the
  bandwidth that is available from each neighbor
  with historical information.
• qki (m) the total number of blocks arrived at
  node i from neighbor k in the mth period.
• Wki(m1) the estimated bandwidth from node k to
  node i

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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

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Distributed Algorithm

• Heuristic distributed algorithm
• Node i estimates the bandwidth Wki(m1) that its
  neighbor k can allocate it in the (m1)th period
  with the traffic received from that neighbor in
  the previous M periods, as shown in equation (3)
• Based on Wki(m1), node i performs the local
  block scheduling (2) using min-cost network flow
  model. The results xkji?0,1 represent whether
  node i should request block j from neighbor k
• Send requests to every neighbor.

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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.

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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

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Simulation Results

• All are DSL nodes with exchanging window of 10
  sec and bottlenecks only at the last mile. Group
  size is 500

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Simulation Results

• All are DSL users with exchanging window of 10
  sec and end-to-end available bandwidth
  10150Kbps. Group size is 500

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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 1080
• 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

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• Thanks
• QA