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  • When you present, try to connect things together. Tell audience the big pictures, look at the screen of your labtop which is placed right in front of you. Do not look at the slides. Eyes contact are important. Remember!
  • The relationship between IPTV and P2P Multimedia streaming system: P2P multimedia streaming is a general technique and IPTV is a subset of P2P multimedia streaming which is used to stream TV content. In general, P2P multimedia streaming is used to stream different kinds of contents such as audio, video, etc. , not only TV content
  • After the third bullet, you need to mention why studying overlay characteristics is important? For future planning of resource usage (use more Proxies to support small overlay or in peak hours to improve churn rate) For future designs of P2P multimedia streaming systems
  • Talk about how a peer joins an overlay and maintain the membership (periodically probe partners for their neighbor list) The overlay is very dynamic and during its session the client probe its partners to get more new peer Maintaining peers in the same channels to get video content is crucial in PPLive because the churn rate is very high (take Keith Ross paper)
  • All previous works use only one machine to crawl How to solve unresponsive peers? 10 crawlers get one IP in 15 seconds any the client joins or leaves the system We believe that PPLive update membership very quickly and therefore we can obtain all peers This is better than previous crawler which only consider responsive peers (in previous work, they state that about 35% of peers is unresponsive). In our experiment, we see that more than 40% of peers are unresponsive Therefore our crawler is more inclusive and obtain correct number of peers.
  • Remove the time you use to collect data but answer when people ask (April – Dec 2006) Confirm that all experiments are validated at least 10 times from the trace collected in different days with different channels Coverage of the crawler: Use 10 machine, each can retrieve 98% of peers over all peers collected by 10 machines Fast Use an experiment: with a machine join and leave the channel, run 10 crawler after 15 seconds, we always can have that IP of the client
  • What is a partner? UDP or TCP connections? We are interested in out-degree of a node (connections from this node to other peers in the same overlay). We test the connection of a PPLive client by using netstat and we see that PPLive client returns almost all current connections (out-degree) when we PING this client. Of course, this client is not inside NAT We only focus on UDP connections when we use script to crawl. However, the connections returned by probed nodes for partner list may be UDP or TCP
  • Remember to define what is Channel size means: a instantaneous number of peers attending a channel More popular channel varies more than less popular channel (Give specific number) This is different from P2P file sharing because P2P file sharing is known as stable system size
  • - Why don’t you extend the experiment to the 3 rd , 4 th , 5 th , … day to see what happen to the channel variation size That is a great suggestion. We have not done so. However, we verify 2 consecutive days with several Movie channels with similar Program Segment lengths and we see that the peaks drift Do the overall system size of PPLive (all channels) is stable overlay (total peers attending the channels) We only measure about 15 channels, not the whole system with > 400 channels. We do not know this answer exactly The peaks at noon and night are not from US and Chinese Time Zones, it is stated in Keith Ross study that the number of Asian users is always from 87 to 98% of peers attending PPLive channels. Therefore, the peaks come from mostly Asian peers
  • Remember to relate the small graph in the left with the big graph, especially the x-axis is in Chinese Time Zone. Please make it clear ! What is small-world behavior? More cluster graph. In graph theory, the random graph is the graph when nodes connect to neighbor randomly, thus the diameter of random graph is largest. Meanwhile, clustered graph like small-world, nodes cluster together  diameter is smaller than that of random graph What is the benefit of this result for future design of the system? Check the recommendation part of the paper One possible answer: small channel is not good for streaming. You may need to place some proxies as caching servers (according to geographic distribution study from Keith Ross, you know where and how many servers should be placed) to support video data so that node can, together with these servers, form clustered graph and share data  support better video streaming  churn rate may decrease
  • What if I extend the experiment into 3 days consecutively? Do you have different result ? Yes, the results may be different. We actually plan to do so in future work. We have not done that experiment. But our result is validated and correct for 24 hours.
  • Why correlated availability? Nodes (users) have the same interests in some video content. They attend some movie and leave when the movie end. Correspondingly, when some favorite movies start, people together join the network What is the application of this correlated availability? shorten start-up delay by connecting node with nodes in the same snapshot before this node is down Create nodes in the same snapshot as a sub-overlay to route content, it is better and improve performance under churn rate.
  • Compute the difference of P(Y=1|X=1) and P(Y=1), if the different is 0 or the two numbers are the same, we can see that the exist of X does not infer of influence the exit of Y. In other words, their appearances are independent.
  • Churn rate, peer arrival/departure effect this significantly because we select random peers from different snapshots over a day
  • You may have a brief explanation about the short session length in PPLive: in PPLive users attend the streaming content similar to users attending TV channels. They switch the channels frequently. Moreover, due to start up delay of the channels, some users switch channel because they can not wait. User of PPLive has behavior very similar to behavior of TV user. This differs very much from users in P2P file sharing where they join the client one time and let the client download content. They can leave the system. In PPLive, user usually turn on the player if they are interested. Otherwise, they leave  this makes the session length is short
  • Need to highlight your modeling part with geometric series session length which can be used for simulation instead of taking the huge amount of traces
  • What is the benefit of this model? Why do you use 9 th degree polynomial?
  • What is the benefit of this model? Why do you use 9 th degree polynomial?
  • Slides

    1. 1. Measurement and Modeling of a Large-scale Overlay for Multimedia Streaming Long Vu, Indranil Gupta, Jin Liang, Klara Nahrstedt University of Illinois at Urbana-Champaign QShine 2007, Vancouver, Canada
    2. 2. Motivation <ul><li>IPTV applications have flourished (SopCast, PPLive, PeerCast, CoolStreaming, TVUPlayer, etc.) </li></ul><ul><li>IPTV growth: ( MRG Inc. April 2007) </li></ul><ul><ul><li>Subscriptions: 14.3 million in 2007, 63.6 million in 2011 </li></ul></ul><ul><ul><li>Revenue: $3.6 billion in 2007, $20.3 billion in 2011 </li></ul></ul><ul><li>Understanding current IPTV (P2P multimedia streaming) applications is crucial for future designs </li></ul>
    3. 3. Motivation <ul><li>PPLive is one of the largest deployed P2P multimedia streaming applications </li></ul><ul><li>Existing studies about PPLive: </li></ul><ul><ul><li>Network-centric : video traffic, flow rate, bandwidth utilization [ X. Hei 06, A. Ali 06, Silverston 07 ] </li></ul></ul><ul><ul><li>User-centric : geographic distribution, user-perceived quality [ X. Hei 06 ] </li></ul></ul><ul><li>However, there is no study about PPLive overlay characteristics : node degree, overlay randomness, node availability, etc. </li></ul>
    4. 4. Study Objectives <ul><li>Evaluate overlay characteristics of PPLive </li></ul><ul><li>Compare overlay characteristics PPLive with P2P file sharing overlay characteristics </li></ul><ul><li>Draw conclusions for future designs and developments </li></ul>
    5. 5. Next… <ul><li>PPLive overview </li></ul><ul><li>Challenges & study methodology </li></ul><ul><li>Results : </li></ul><ul><ul><li>Channel size variation </li></ul></ul><ul><ul><li>Node degree </li></ul></ul><ul><ul><li>Overlay randomness </li></ul></ul><ul><ul><li>Node availability </li></ul></ul><ul><ul><li>Session length </li></ul></ul><ul><li>Compare PPLive and P2P file sharing </li></ul><ul><li>Draw conclusions </li></ul>
    6. 6. PPLive Overview <ul><li>One of the largest deployed P2P multimedia streaming systems </li></ul><ul><li>Developed in China </li></ul><ul><li>Hundred thousands of simultaneous viewers </li></ul>
    7. 7. PPLive Episode Channels <ul><li>Most popular (number of viewers) </li></ul>A Program Segment (PS) An episode channel
    8. 8. PPLive Membership Protocol
    9. 9. Challenges <ul><li>PPLive is a closed-source system </li></ul><ul><li>How to develop the measurement method to measure PPLive overlay </li></ul><ul><li>How to select the right metrics to measure PPLive overlay characteristics </li></ul><ul><li>Many PPLive peers are unresponsive </li></ul>
    10. 10. Our Approaches <ul><li>Capture traffic, analyze messages </li></ul><ul><li>Develop a crawler-based study methodology </li></ul><ul><li>Studied Metrics : </li></ul><ul><ul><li>Channel population size </li></ul></ul><ul><ul><li>Node degree </li></ul></ul><ul><ul><li>Overlay randomness </li></ul></ul><ul><ul><li>Node availability </li></ul></ul><ul><ul><li>Node session length </li></ul></ul>
    11. 11. Crawler <ul><li>10 PlanetLab geographically distributed nodes to crawl peers ( Previous works use only one machine ) </li></ul><ul><li>Aggregate, de-duplicate crawled peers to get the peer list </li></ul>
    12. 12. Operations Snapshot collects peers in one channel PartnerDiscovery collects partners of responsive peers Studied channels
    13. 13. Next… <ul><li>PPLive overview </li></ul><ul><li>Challenges & study methodology </li></ul><ul><li>Results : </li></ul><ul><ul><li>Channel size variation </li></ul></ul><ul><ul><li>Node degree </li></ul></ul><ul><ul><li>Overlay randomness </li></ul></ul><ul><ul><li>Node availability </li></ul></ul><ul><ul><li>Session length </li></ul></ul><ul><li>Compare PPLive and P2P file sharing </li></ul><ul><li>Draw recommendations </li></ul>
    14. 14. Channel Size Varies over a day <ul><li>Peaks at noon and night </li></ul><ul><li>A varies 10 times, B and C varies 2 times </li></ul><ul><li>Different from P2P file sharing [ Bhagwan 03 ] </li></ul>
    15. 15. Channel Size Varies over Consecutive Days <ul><li>The same channel, same program: Peaks drift </li></ul><ul><li>Peaks depend on time and channel content </li></ul>First day Second day
    16. 16. Node Degree is Independent of Channel Size <ul><li>Similar to P2P file sharing [ Ripeanu 02 ] </li></ul>Average node degree scale-free
    17. 17. Overlay Randomness <ul><li>Clustering Coefficient (CC) [Watts 98] </li></ul><ul><ul><li>for a random node x with two neighbors y and z , the CC is the probability that either y is a neighbor of z or vice versa </li></ul></ul><ul><li>Probability that two random nodes are neighbors (D) </li></ul><ul><ul><li>Average degree of node / channel size </li></ul></ul><ul><li>Graph is more clustered if CC is far from D [ well-known results from theory of networks and graphs ] </li></ul>
    18. 18. Overlay Randomness May Depend on Channel Size <ul><li>Small overlay, more random </li></ul><ul><li>Large overlay, more clustered </li></ul>Different from P2P file sharing (small-world behavior) [ Ripeanu 02, Saroiu 03 ]
    19. 19. Node Availability (1) [Bhagwan 03] <ul><li>Number of peer pairs (X,Y): </li></ul><ul><li>Compute P(Y=1|X=1). E.g. P(Y=1|X=1) = 2/3 </li></ul><ul><li>12 hours: 72 snapshots, 24 hours: 144 snapshots </li></ul>Pick 185 random peers from one snapshot …
    20. 20. Nodes in one Snapshot Have Correlated Availability <ul><li>Different from P2P file sharing [Bhagwan 03] </li></ul>Correlated Availability Nodes appearing together is likely appear together again
    21. 21. Node Availability (2) <ul><li>Compute the difference of P(Y=1|X=1) and P(Y=1) </li></ul><ul><li>For example: P(Y=1|X=1) = 1/3 ; P(Y=1) = 1/2 </li></ul>Pick 500 random peers from all peers of the whole day (144 snapshots) …
    22. 22. Random Node Pairs Have Independent Availabilities <ul><li>Similar to P2P file sharing [Bhagwan 03] </li></ul>Independent Availabilities
    23. 23. PPLive Peers are Impatient 50% sessions are less than 10 minutes Different from P2P file sharing [ Saroiu 03 ]
    24. 24. Geometric Series Session Length
    25. 25. Comparison
    26. 26. Conclusions <ul><li>Overlay characteristics of PPLive and P2P file sharing are very different </li></ul><ul><li>PPLive channel population size depends on time and channel content </li></ul><ul><li>Nodes in one snapshot have correlated availability : </li></ul><ul><ul><li>route media streams (shorten startup delay) </li></ul></ul><ul><ul><li>create sub-overlay to share content </li></ul></ul><ul><li>Simulation of P2P multimedia streaming needs to take the node availability into account </li></ul><ul><li>Session Lengths are short and follow Geometric series </li></ul>
    27. 27. Thank you!
    28. 28. Backup slides
    29. 29. Responded Peers <ul><li>More than 50% of peers do not respond to PING messages </li></ul>
    30. 30. Membership Update <ul><li>Experiment 1 : A UIUC PPLive client attends a channel </li></ul><ul><ul><li>Turn off the UIUC client </li></ul></ul><ul><ul><li>After 10 seconds, use 10 PlanetLab crawlers to crawl that channel, there is no IP of the UIUC client </li></ul></ul><ul><li>Experiment 2 : </li></ul><ul><ul><li>A UIUC PPLive client joins a PPLive channel </li></ul></ul><ul><ul><li>After 10 seconds, use 10 PlanetLab crawlers to crawl that channel, there exists IP of the UIUC client </li></ul></ul><ul><li>Conclusion: PPLive membership update is very fast </li></ul><ul><li>Unresponsive peers are not dead peers </li></ul>
    31. 31. Active Peers <ul><li>Given an overlay (or a channel) G: </li></ul>
    32. 32. Channel Size Varies over a day <ul><li>Coefficients match </li></ul>
    33. 33. Channel Size Varies over a day

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