An Alliance based Peering Scheme for P2P Live Media Streaming

1. An Alliance based PeeringScheme for P2P Live Media Streaming Darshan Purandare Ratan Guha University of Central Florida IEEE TRANSACTIONS ON MULTIMEDIA

2. outline • Introduction • BEAM model • BEAM & Small World Network • Graph theoretic analysis of BEAM model • Simulation results • Conclusions

3. Introduction • 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

4. Introduction - Current Issues • Quality of Service can improve [Hei et al. 06] • Long start up time • Peer Lag • Unfairness [Ali et al. 06] • Lack tit-for-tat fairness • Uplink bandwidth distribution uneven • Sub-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

5. 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

6. 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

7. 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

8. BEAM – power node • Power nodes changes periodically based on Utility 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

9. BEAM - Alliance Formation • Nodes cluster in groups of 4-8 to form alliances • A node can be a member of multiple alliances • h: Max number of nodes in an Alliance • K: 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

10. BEAM - Alliance Formation Peerlist of Node 1 :: 6, 17, 23 Peerlist of Node 6 ::12, 22, 43

11. 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

12. Media server 1 2 3 4 Stream packet BEAM - Alliance Functionality h = 5 K = 2 Alliance 2 Alliance 1

13. 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]

14. 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

15. Cv( )= 1/3 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 kvneighbors ,at most kv (kv-1)/2 edges • Cv: • C(p) :average of Cvover all v • how well my neighbors are connected to each other

16. 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)

17. BEAM & SWN • 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

18. 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

19. Graph theoretic analysis of BEAM model • In a steady state, when all the nodes have formed k alliances, and each alliance has exactly h members • M Super nodes

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. QoS: Average Jitter Rate

27. QoS: Average Latency

28. Uplink Utilization

29. Fairness: Share Ratio Range

30. 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