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An Alliance based PeeringAn Alliance based Peering Scheme Scheme for P2P Live Media Streamingfor P2P Live Media Streaming
Darshan PurandareDarshan Purandare Ratan GuhaRatan Guha
University of Central FloridaUniversity of Central Florida
IEEE TRANSACTIONS ON MULTIMEDIAIEEE TRANSACTIONS ON MULTIMEDIA
outlineoutline
Introduction BEAM model BEAM & Small World Network Graph theoretic analysis of BEAM model Simulation results Conclusions
IntroductionIntroduction
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 NICE & ZIGZAG End System Multicast (ESM) PRIME CoolStreaming /DONet
Introduction - Introduction - Current Issues
Quality of Service Quality of Service can improve [Hei et al. 06] Long start up time Peer Lag
Unfairness Unfairness [Ali et al. 06] Lack tit-for-tat fairness Uplink bandwidth distribution uneven
Sub-optimal uplink utilizationSub-optimal uplink utilization May affect QoS & Scalability
Peer A can download data from peer B if:(bytes downloaded from B - bytes uploaded to B)
< threshold
BEAM model
BEAM: Bit strEAMing Consists of three main entities
Nodes Media relaying server
Origin of the stream content in the swarm
Tracker A server that assists nodes in the swarm to communicate
with other peers
BEAM model
New user arrive Contacts the Tracker
submits its IP address together with its bandwidth range Obtains peerlist from Tracker
contains nodes in similar bandwidth range
(typically 40 nodes) similar bandwidth range -> optimal resource utilization
Server relays stream content to Power nodes bottleneck in its uplink speed
BEAM – power node
power node : higher contribution to the swarm in terms of content served 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
Power nodes changes periodically based on Utility FactorUtility Factor (UF)
A node’s UF computed using: Cumulative share ratio (CSR)
Temporal share ratio (TSR)
UF = α CSR + (1-α) TSR Only the nodes that have UF 2.0 periodically update ≧
the tracker
BEAM - Alliance Formation
Nodes cluster in groups of 4-8 to form alliances A node can be a member of multiple alliances
hh: Max number of nodes in an Alliance KK: Max number of alliances a node can join
A node creates an alliance send join request -> nodes in its peer list receiving node accept or reject
how many alliances it is currently a member of
BEAM - Alliance Formation
Peerlist of Node 1 :: 6, 17, 236, 17, 23
Peerlist of Node 6 :: 12, 22, 4312, 22, 43
BEAM - Alliance Functionality
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 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 FunctionalityMedia server
1 2 3
4Stre
am packet
Alliance 1Alliance 2
h = 5K = 2
BEAM & Small World Network
Why form Alliances ? 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][Watts et al., Nature,98]
Small World Network
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
characteristic path length L(p) 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)
vertex v has kv neighbors ,at most kv (kv-1)/2 edges Cv :
C(p) :average of Cv over all v how well my neighbors are connected to each other
edges possible total
edges of # actual
Cv( )= 1/3
Small World Network
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 High clustering C(p)/C(0) Short path length L(p)/L(0)
Suppose a node is a member of k alliances
and each alliance has neighbors
,where and
Ex. h = 5 , k = 2 Much higher than a random graph Same size random graph Cv = 0.0019
BEAM & SWN
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Graph theoretic analysis of BEAM model
Graph density is an important factor for the connectedness of a graph
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
edges possible ofnumber total
edges ofnumber D
In a steady state, when all the nodes have formed k alliances, and each alliance has exactly h members
M Super nodes
Graph theoretic analysis of BEAM model
h
NkM
1
)1(
N
khDgraph
)1(
)1(
2
)1( 11
2
11
NN
hhD ij
kj
Ni
N
ijkj
Ni
graph
Graph theoretic analysis of BEAM model
outdegree of a super node
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
hNk
kh
1
2
M2
MO
D2
alliance
D D graph alliance
)1( khO
Simulation detail 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
Membership manager mCache: record partial
list of other active nodes Partnership manager
Random select Transmission scheduler
Schedules transmission of video data
Buffer Map Record availability
BM representation and exchange
A video length is divided into segments of uniform size
Availability of the segments in a node is represented by a Buffer Map (BM) 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
Calculate the number of potential suppliers for each segment Message exchange
Window-based buffer map (BM): data availability Segment request (similar to BM)
Less supplier first Multi-supplier: highest bandwidth within deadline
first
Simulation Details
Streaming rate = 512 Kbps
Media Server’s Uplink = 1536 Kbps (3 links)
Heterogeneous bandwidth class (512,128), (768,256), (1024, 512), (1536,768),
(2048, 1024)
H, K = 4, 2 (6 neighbor nodes)
Each node buffers content for 120 sec
QoS: Average Jitter Rate
QoS: Average Latency
Uplink Utilization
Fairness: Share Ratio Range
Conclusions
Alliance based peering scheme is an effective
technique to group peers
QoS, Uplink throughput and fairness results are
at par or even better than CoolStreaming
Peer lag can be improved using BEAM
Initial buffering time can be slightly improved
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