BitTorrent Under a Microscope:Towards Static QoS Provision in Dynamic Peer-to-Peer Networks

Tom H. Luan*, Xuemin (Sherman) Shen* and Danny H. K. Tsang* University of Waterloo

Hong Kong University of Science and Technology

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2 BT Under a MicroscopeIWQoS’10

BT, first appeared in October 2002, is a file distribution system based on the P2P paradigm

Engrosses about 30% of all Internet traffic volume [1]

Leads to the proliferation of P2P media streaming using the user-driven data-oriented download approach For example, CoolStreaming, PPLive [2] and PPStream for live

and on-demand video streaming PPlive is reported in [2] to broadcast to over 200,000 users in

one event at the bit rate of 400-800 kbps Successful media streaming requires providing users

with the static and guaranteed download throughput

BitTorrent (BT): A Brief Introduction

[1]. EContentMag.com, “Chasing the user: The revenue streams of 2006”, December 2005[2]. Xiaojun Hei, Chao Liang, Jian Liang, Yong Liu and Keith W. Ross, "A Measurement Study of a Large-Scale P2P IPTV System", IEEE Transactions on Multimedia, vol. 9, no. 8, pp. 1672 - 1687, Dec. 2007.

3 BT Under a MicroscopeIWQoS’10

QoS provisioning is tough in P2P P2P network is inherently dynamic and heterogeneous

The heterogeneous bandwidth of peer uploaders results in the unpredictable download throughput of nodes

The dynamic nature of peer uploaders results in the intense variance (or jitters) of download throughput to nodes

Problem Statement: How to accommodate the bandwidth heterogeneity and dynamics of peers to provision nodes with static and guaranteed download throughput?

Methodology: Evaluate and enhance the performance of BT

QoS in P2P Content Distribution

4 BT Under a MicroscopeIWQoS’10

BT strives to ensure (proportional) fairness: Nodes attain the download rates proportional to their upload rates Incentive mechanism to encourage the upload

BT Protocol

Tit-for-Tat scheme (Forbid freeriders) Each node only uploads to others who

are uploading to it Choking algorithm (Preserve the

high-rate uploaders) Every Tc (e.g., 10) seconds, select nc (e.g, 4) nodes to unchoke (upload to) among the peers which are uploading to it Optimistic unchoke (Explore the high-

rate nodes for data exchange) Randomly unchoke no (e.g., 1) node

which is not uploading to it every To (e.g, 30) seconds

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Example of the Node Connectivity

Data exchange governed by tit-for-tat and choking algorithm

Download from others via optimistic unchoke of others

Upload to others with its optimistic unchoke

Fixed number of upload connections

Random number of download connections

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Assuming two classess of peers, high bandwidth (H-BW) and low bandwidth peers

Model the download connections of a randomly tagged node in class as a Markov process with state Downloading from H-BW nodes and L-BW nodes

Download rate at time t

Asymptotically, the mean and variance of are, respectively,

Throughput Analysis of a Random BT Node

Hc Lc

))(),(( tYtX

x y

),( yx

)(td

and

N

Hp Lp

, Upload capacity of H-BW and L-BW nodes, respectively.

Mean population of peers.

, Portion of H-BW and L-BW nodes, respectively. HL pp 1Steady state of the Markov process

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Transition rates are composed of three events Dynamic node arrivals and departures Connections/disconnections due to the choking

algorithm Connections/disconnections due to the optimistic

unchoke Obtain the steady state probability with the

balance equations

Numerical Solution

where is the transition rate matrix of the node in class

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Model Validation Session level simulator coded in C++ Poisson arrival to the network at the rate of

peers/s Mean network size to be N Nodal departure rate Each experiment with 30 simulation runs

and 95% confidence interval

N/

9 BT Under a MicroscopeIWQoS’10

Highly dynamic due to peer churns and the frequent disconnection of choking algorithm and optimistic unchoke

Download rate is proportional to upload rate

Download Rate of Tagged Node over Time

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Increasing nc and no

nc: connections in the choking algorithmno: connections in the optimistic unchoke

Our model is more accurate to capture the dynamic nature of P2P Increasing nc improves the fairness Increasing no degrades the fairness

Fan: Fan, B., Chiu, D.-M., and Lui, J. “Stochastic analysis and file availability enhancement for BT like file sharing systems”, In proc. of IEEE IWQoS, 2006

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Increase Tc and Arrival Rate

To = 3Tc : Time interval for executing optimistic algorithm

Increasing Tc degrades the fairness as nodes are slow to adapt

Increase arrival rate degrades the fairness as the network becomes more chaos

Tc : Time interval for executing choking algorithm

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Given the peer arrival rate and mean network size, we can optimize the parameters of BT towards maximal fairness as Parameters including: number of links and

execution frequency for choking algorithm, and those of optimistic unchoke

Rather than fine tune the parameters, can we improve the protocol for better performance? Enhanced protocol for better QoS provisioning

Optimize BT Parameters

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BT relies on node clustering to provision QoS Nodes of similar upload capacity tend to form

clusters to exchange data

Node Clustering in BT

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Protocol Enhancement What is wrong with the clustering in BT?

Optimistic unchoke: blind search Randomly connect to nodes in the peer ocean to

explore high rate nodes Choking algorithm: a trail-and-error manner Time to locate appropriate cluster peers is long cluster effect is weak in a highly heterogeneous

and dynamic network Random walk based peer selection

Efficiently and fast search cluster nodes

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Link Level Homogeneity Form the graph in which nodes have equal

capacity per out-degree Make outgoing connections of nodes proportional

to their upload capacity With TCP connection, bandwidth is equally

allocated to upload connections Random walk algorithm to search peers with

high capacity per out-degree value Guaranteed fairness: each

connection is bidirectional, downloading and uploading at the same rate

Simulation A more heterogeneous network with capacity

distribution

where

Download rate of the tagged node over simulation time

Enhanced BT with random walk

Approaches to the upload capacity with vary small variations in the dynamic network

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Validation of Link-level Homogeneity

Over 75% of peers have equal capacity per upload connection, with the value same to the analysis

Change the upload capacity of the tagged node every 1000 seconds

In practice, upload capacity is shared by multiple applications 17 BT Under a Microscope IWQoS’10

Conclusions To provision static and accurate QoS

guarantee is a fundamental and important issue for P2P content distribution networks (e.g., BT, PPStream) How to address the network dynamic and

heterogeneity We propose a Markov model to evaluate the

download rate of a randomly selected BT node Throughput in the dynamic and heterogeneous

network Describe an enhanced BT protocol with

efficient peer selection using the random walk algorithm The Blind trial-and-error search is inefficient

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Thank You !

Q & A