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 § §

 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 kbps  Successful media streaming requires providing users with the static and guaranteed download throughput 2BT Under a MicroscopeIWQoS’10 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 , Dec

 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 3BT Under a MicroscopeIWQoS’10 QoS in P2P Content Distribution

 BT strives to ensure (proportional) fairness: Nodes attain the download rates proportional to their upload rates  Incentive mechanism to encourage the upload 4BT Under a MicroscopeIWQoS’10 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

5BT Under a MicroscopeIWQoS’10 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

 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, 6BT Under a MicroscopeIWQoS’10 Throughput Analysis of a Random BT Node and, Upload capacity of H-BW and L-BW nodes, respectively. Mean population of peers., Portion of H-BW and L-BW nodes, respectively. Steady state of the Markov process

 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 7BT Under a MicroscopeIWQoS’10 Numerical Solution where is the transition rate matrix of the node in class

8BT Under a MicroscopeIWQoS’10 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

 Highly dynamic due to peer churns and the frequent disconnection of choking algorithm and optimistic unchoke  Download rate is proportional to upload rate 9BT Under a MicroscopeIWQoS’10 Download Rate of Tagged Node over Time

10BT Under a MicroscopeIWQoS’10 Increasing n c and n o  n c : connections in the choking algorithm n o : connections in the optimistic unchoke  Our model is more accurate to capture the dynamic nature of P2P  Increasing n c improves the fairness  Increasing n o 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

11BT Under a MicroscopeIWQoS’10 Increase T c and Arrival Rate  T o = 3T c : Time interval for executing optimistic algorithm  Increasing T c degrades the fairness as nodes are slow to adapt  Increase arrival rate degrades the fairness as the network becomes more chaos  T c : Time interval for executing choking algorithm

 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 12BT Under a MicroscopeIWQoS’10 Optimize BT Parameters

 BT relies on node clustering to provision QoS  Nodes of similar upload capacity tend to form clusters to exchange data 13BT Under a MicroscopeIWQoS’10 Node Clustering in BT

14BT Under a MicroscopeIWQoS’10 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

15BT Under a MicroscopeIWQoS’10 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 16BT Under a MicroscopeIWQoS’10

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 17BT Under a MicroscopeIWQoS’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 18BT Under a MicroscopeIWQoS’10

Thank You ! 19BT Under a MicroscopeIWQoS’10 Q & A