An Alliance based PeeringScheme for P2P Live Media Streaming An Alliance based Peering Scheme for...

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  • Slide 1
  • An Alliance based PeeringScheme for P2P Live Media Streaming An Alliance based Peering Scheme for P2P Live Media Streaming Darshan Purandare Ratan Guha University of Central Florida IEEE TRANSACTIONS ON MULTIMEDIA
  • Slide 2
  • outline Introduction BEAM model BEAM & Small World Network Graph theoretic analysis of BEAM model Simulation results Conclusions
  • Slide 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
  • Slide 4
  • 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 utilization 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
  • Slide 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
  • Slide 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
  • Slide 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
  • Slide 8
  • BEAM power node Utility Factor Power nodes changes periodically based on Utility Factor (UF) A node s UF computed using: Cumulative share ratio (CSR) Temporal share ratio (TSR) UF = CSR + (1-) TSR Only the nodes that have UF 2.0 periodically update the tracker
  • Slide 9
  • BEAM - Alliance Formation Nodes cluster in groups of 4-8 to form alliances A node can be a member of multiple alliances h h: Max number of nodes in an Alliance K 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
  • Slide 10
  • BEAM - Alliance Formation 6, 17, 23 Peerlist of Node 1 :: 6, 17, 23 12, 22, 43 Peerlist of Node 6 :: 12, 22, 43
  • Slide 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
  • Slide 12
  • BEAM - Alliance Functionality Media server 1 2 3 4 Stream packet Alliance 1 Alliance 2 h = 5 K = 2
  • Slide 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] [Watts et al., Nature,98]
  • Slide 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
  • Slide 15
  • Small World Network characteristic path length L(p) L v :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 k v neighbors,at most k v (k v -1)/2 edges C v : C(p) :average of C v over all v how well my neighbors are connected to each other C v ( )= 1/3
  • Slide 16
  • Small World Network n = 1000 vertices, average degree of k = 10 edges per vertex For a range of ps with 0 < p < 1,the SWN G(p) is characterized by High clustering C(p)/C(0) Short path length L(p)/L(0)
  • Slide 17
  • 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 C v = 0.0019 BEAM & SWN
  • Slide 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 D graph :density of the graph D alliance :density of the graph when alliances are abstracted as vertices i.e., super nodes as vertices
  • Slide 19
  • 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
  • Slide 20
  • outdegree of a super node For h=5,k = 2 Node degree = (h-1) * k =8, N =512 D graph = 0.004,D alliance = 0.025 Density of the graph at alliance level is relatively much higher than at the node level
  • Slide 21
  • Simulation detail Compare the behavior of BEAM with CS CS (CoolStreaming/DONet) DONet: Data-driven Overlay Network Dont 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
  • Slide 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
  • Slide 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
  • Slide 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
  • Slide 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
  • Slide 26
  • QoS: Average Jitter Rate
  • Slide 27
  • QoS: Average Latency
  • Slide 28
  • Uplink Utilization
  • Slide 29
  • Fairness: Share Ratio Range
  • Slide 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