Download An Alliance Based Peering Scheme for P2P Live Media Streaming

An Alliance based Peering Scheme
for P2P Live Media Streaming
Darshan Purandare
Ratan Guha
University of Central Florida
IEEE TRANSACTIONS ON MULTIMEDIA
outline
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Introduction
BEAM model
BEAM & Small World Network
Graph theoretic analysis of BEAM model
Simulation results
Conclusions
Introduction
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with the advent of multimedia technology, there
has been an increasing use of P2P networks
Various paradigms for P2P streaming have been
proposed
Most overlay network construction algorithms
form a tree like node topology
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NICE & ZIGZAG
End System Multicast (ESM)
PRIME
CoolStreaming /DONet
Introduction - Current Issues
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Quality of Service can improve [Hei et al. 06]
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Unfairness
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Long start up time
Peer A can download data from peer B if:
Peer Lag
(bytes downloaded from B - bytes uploaded to B)
[Ali et al. 06] < threshold
Lack tit-for-tat fairness
Uplink bandwidth distribution uneven
Sub-optimal uplink utilization
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May affect QoS & Scalability
BEAM model
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BEAM: Bit strEAMing
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Consists of three main entities
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Nodes
Media relaying server
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Origin of the stream content in the swarm
Tracker
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A server that assists nodes in the swarm to communicate
with other peers
BEAM model
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New user arrive
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Contacts the Tracker
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Obtains peerlist from Tracker
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submits its IP address together with its bandwidth range
contains nodes in similar bandwidth range
(typically 40 nodes)
similar bandwidth range -> optimal resource utilization
Server relays stream content to Power nodes
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bottleneck in its uplink speed
BEAM – power node
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power node : higher contribution to the swarm in
terms of content served
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Initially, chosen from the nodes with higher uplink
bandwidth
tracker periodically (e.g., every 10 min) computes the
rank of the nodes
updates the media server
BEAM – power node
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Power nodes changes periodically based on
Utility Factor (UF)
A node’s UF computed using:
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Cumulative share ratio (CSR)
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Temporal share ratio (TSR)
UF = α CSR + (1-α) TSR
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Only the nodes that have UF ≧2.0 periodically update
the tracker
BEAM - Alliance Formation
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Nodes cluster in groups of 4-8 to form alliances
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A node can be a member of multiple alliances
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h: Max number of nodes in an Alliance
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K: Max number of alliances a node can join
A node creates an alliance
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send join request -> nodes in its peer list
receiving node accept or reject
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how many alliances it is currently a member of
BEAM - Alliance Formation
Peerlist of Node 1 :: 6, 17, 23
Peerlist of Node 6 :: 12, 22, 43
BEAM - Alliance Functionality
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A node can be a member of multiple alliances ->
multiple paths for a node to obtain the stream content in
case of node failures
A member procure a new packet , it propagates within its
alliances
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all the members of a alliance request all the pieces
 Serves distinct pieces to its peers ((h-1)pieces)
 Peers exchange the pieces among them selves
A node requests specific unavailable pieces
 Forwarding node sends only request pieces
BEAM - Alliance Functionality
h=5
K=2
Alliance 1
Media server
Alliance 2
BEAM & Small World Network
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Why form Alliances ?
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Clustering into alliances forms a small world network
graph
Dense local clustering (high clustering coefficient)
Some links to other part of the graph (non local)
Overlay distance Is near-optimal
Robust to network perturbations such as churn
[Watts et al., Nature,98]
Small World Network
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choose a vertex and the edge
With probability p, we reconnect edge to a vertex chosen uniformly
at random over the entire ring
p = 0, the original ring is unchanged
p increases, the graph becomes increasingly disordered
p = 1, all edges are rewired randomly.
intermediate values of p, the graph is a small-world network
Small World Network
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characteristic path length L(p)
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Lv :number of edges between two vertices
L(p):averaged over all pairs of vertices
average number of friendships in the shortest chain
connecting two people
clustering coefficient C(p)
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vertex v has kv neighbors ,at most kv (kv-1)/2 edges
Cv : actual # of edges
total possible edges
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Cv(
)= 1/3
C(p) :average of Cv over all v
how well my neighbors are connected to each other
Small World Network
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n = 1000 vertices, average degree of k = 10 edges per
vertex
For a range of p’s with 0 < p < 1,the SWN G(p) is
characterized by
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High clustering C(p)/C(0)
Short path length L(p)/L(0)
BEAM & SWN
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Suppose a node is a member of k alliances
a1, a2, a3...ak  and each alliance has neighbors
m1, m2, m3...mk  ,where mi  h and 1  i  k
Cv 
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
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m1  m 2  ...  mk 
2   2   2 
 m1  m 2  m3  ...  mk 
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2
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Cv 
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4   4 
2   2  3
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 0.428
8
 
7
 2
 
Ex. h = 5 , k = 2
Much higher than a random graph
Same size random graph Cv = 0.0019
Graph theoretic analysis of
BEAM model
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Graph density is an important factor for the
connectedness of a graph
D
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number of edges
total number of possible edges
We evaluate the graph density of a BEAM graph by
abstracting the alliances as nodes (super node)
N nodes in the swarm ,spread in M alliances
Dgraph :density of the graph
Dalliance :density of the graph when alliances are
abstracted as vertices i.e., super nodes as vertices
Graph theoretic analysis of
BEAM model
Dgraph
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 
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N
i 1
(hij  1)
k
j 1
N
2
2
 
 
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N
i 1
k
j 1
(hij  1)
N  ( N  1)
In a steady state, when all the nodes have formed k
alliances, and each alliance has exactly h members
(h  1) k
Dgraph 
N 1
M Super nodes
Nk
M
h
Graph theoretic analysis of
BEAM model
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outdegree of a super node
O  h(k  1)
MO
2
k  1
h
Dalliance  2 
 M  Nk  h 
 
2
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For h=5 ,k = 2
Node degree = (h-1) * k =8 , N =512
Dgraph = 0.004 ,Dalliance = 0.025
Density of the graph at alliance level is relatively much
higher than at the node level
Dalliance  Dgraph
Simulation detail
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Compare the behavior of BEAM with CS
CS (CoolStreaming/DONet)
 DONet: Data-driven Overlay Network
 Don’t use any tree, mesh, or any other structures
 CoolStream: Cooperative Overlay Streaming
 A practical DONet implementation
Node periodically exchanges data availability information
with partners
Retrieve unavailable data from one or more partners, or
supply available data to partners
The more people watching the streaming data, the better
the watching quality will be
Diagram for a DONet node
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Membership manager
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Partnership manager
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Random select
Transmission scheduler
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mCache: record partial
list of other active nodes
Schedules transmission
of video data
Buffer Map
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Record availability
BM representation and exchange
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A video length is divided into segments of
uniform size
Availability of the segments in a node is
represented by a Buffer Map (BM)
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In practical, a BM is recorded by 120 bits for 120
segments
Each node continuously exchanges its BM with
its partners and schedules which segments to
fetch from which partner
Scheduling algorithm
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Calculate the number of potential suppliers
for each segment
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Message exchange
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Window-based buffer map (BM): data availability
Segment request (similar to BM)
Less supplier first
Multi-supplier: highest bandwidth within deadline
first
Simulation Details
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Streaming rate = 512 Kbps
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Media Server’s Uplink = 1536 Kbps (3 links)
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Heterogeneous bandwidth class
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(512,128), (768,256), (1024, 512), (1536,768),
(2048, 1024)
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H, K = 4, 2 (6 neighbor nodes)
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Each node buffers content for 120 sec
QoS: Average Jitter Rate
QoS: Average Latency
Uplink Utilization
Fairness: Share Ratio Range
Conclusions
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Alliance based peering scheme is an effective
technique to group peers
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QoS, Uplink throughput and fairness results are
at par or even better than CoolStreaming
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Peer lag can be improved using BEAM
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Initial buffering time can be slightly improved