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

1. 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 §

2. BitTorrent (BT): A Brief Introduction • 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 [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. BT Under a Microscope IWQoS’10

3. QoS in P2P Content Distribution • QoS provisioning is tough in P2P • P2P network is inherently dynamicandheterogeneous • 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 BT Under a Microscope IWQoS’10

4. BT Protocol • BT strives to ensure (proportional) fairness: Nodes attain the download rates proportional to their upload rates • Incentive mechanism to encourage the upload • 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 nodesfor data exchange) • Randomly unchokeno (e.g., 1) node which is not uploading to it every To (e.g, 30) seconds BT Under a Microscope IWQoS’10

5. Example of the Node Connectivity • Fixed number of upload connections • Random number of download connections Download from others via optimistic unchoke of others Upload to others with its optimistic unchoke Data exchange governed by tit-for-tat and choking algorithm BT Under a Microscope IWQoS’10

6. Throughput Analysis of a Random BT Node • 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, , 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 and BT Under a Microscope IWQoS’10

7. Numerical Solution • 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 where is the transition rate matrix of the node in class BT Under a Microscope IWQoS’10

8. 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 BT Under a Microscope IWQoS’10

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

10. Increasing nc and no • nc: connections in the choking algorithm no: 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 BT Under a Microscope IWQoS’10

11. 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 BT Under a Microscope IWQoS’10

12. Optimize BT Parameters • 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 BT Under a Microscope IWQoS’10

13. Node Clustering in BT • BT relies on node clustering to provision QoS • Nodes of similar upload capacitytend to form clusters to exchange data BT Under a Microscope IWQoS’10

14. 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 BT Under a Microscope IWQoS’10

15. 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 BT Under a Microscope IWQoS’10

16. 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 BT Under a Microscope IWQoS’10

17. 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 BT Under a Microscope IWQoS’10

18. 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 BT Under a Microscope IWQoS’10

19. Q & A Thank You ! BT Under a Microscope IWQoS’10