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

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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

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NN

hhD ij

kj

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ijkj

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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