Alex Bikfalvi  Jaime García-Reinoso  Iván Vidal Francisco Valera  Arturo Azcorra

1. Peer-to-Peer vs. IP MulticastComparing Approaches to IPTV Streaming Based on TV Channel Popularity Alex Bikfalvi  Jaime García-Reinoso  Iván Vidal Francisco Valera  Arturo Azcorra

2. Commercial-grade IPTV • How some telcos stream IPTV? IPTV broadcast server Backbone network IP multicast (static) DSLAM Customer premise DSLAM xDSL DSLAM xDSL xDSL Customer premise IGMP: 1-2 channels xDSL ADSL router Set-top box Customer premise Networking Seminar TV set

3. Motivation • Most deployment are walled-gardens • Multicast has been the preferred technical solution • Current/possible future tends… • Next generation networks, open to third-party providers • Studies show that over 90 % of channels are watched by 20% of subscribers • Semi-interactive techniques: NVoD • User generated content • Possible issues for the telcos • Is it still affordable to use multicast? • Even for very unpopular channels? Networking Seminar

4. What are we doing Let’s compare IP multicast with an alternative: Peer-to-Peer • Why P2P? • Telcos can leverage their set-top boxes to form a P2P overlay • Main question • How the TV channel popularity affects the difference in performance • Dimensions of our analysis • Bandwidth utilization • Multicast scalability Networking Seminar

5. The streaming TV watching The network Setting up the foundation Networking Seminar

6. Streaming scenario • Hybrid: IP multicast and P2P-based unicast • 100 TV channels • P2P-based unicast • Set-top boxes (STBs) are peers • Channel stream is pulled/pushed from/by other STB(s) • The head-end server is a last resort g N–g IP multicast connections (P2P) unicast connections N TV channels Networking Seminar

7. P2P overlay • A P2P algorithm handles peer discovery • I.e. another STB receiving the same channel • Another dimension to the problem: locality • Algorithm effectiveness: P2P ratio Number of peers connected to peers Number of peers Number of peers connected to the server Networking Seminar

8. Watching TV • Modeling the user behavior • How long a user watches a TV channel: channel holding time (CHT) • TV channel popularity • TV channel zappingprobability • TV channel number of viewers • The model • Input: CHT and popularity • Output: zapping probability • 10000 users and limited number of popularity levels Networking Seminar

9. Popularity model • What is the channel popularity? • How often users arrive/leave • How long they watch the channel • It sums the CHTs of all viewers during the observation period • The popularity of all channels: Number of viewers Observation period Time Popularity: Networking Seminar

10. Zapping probability • The probability of changing to a TV channel • Relationship with popularity j n Sufficiently large observation period and all channels have the same probability distribution of the channel holding time i k m l Popularity of channel i Zapping probability of channel i Popularity of all channels Networking Seminar

11. Viewers • The average number of users watching a channel Number of viewers Observation period Time Popularity: Number of users (10000) Observation period Networking Seminar

12. Our model • Define channel popularity levels • Abstract, not based on a measurement • The effects to be easy identifiable • If possible, popularity to translate in easy zapping decisions • CHT: measurement study (Cha et al.) Networking Seminar

13. Network topology • Access network like DSL • One link (hop) from backbone to customer premise • Backbone network using BRITE • 100 routers / 50 edge routers • Ratio edges-to-nodes (m): 1, 2, 3, 4 • Average path length between two nodes: lu • Average multicast tree size from a source to a group of g nodes: lm= f(g) Networking Seminar

14. More on multicast trees • Tree size vs. group size • When g much smaller than the number of edge routers: power-law (Chuang and Sirbu) • When g much larger than the number of edge routers:constant Tree size (lm) Here we explore the P2P alternative Here IP multicast is really worth the buck Group size (g) Number of edge routers Networking Seminar

15. So for our backbone… • Set of measurements: • Random sources • Random groups Worse connected network Saturation Better connected network Power-law Networking Seminar

16. Analytical estimation Bandwidth utilization Networking Seminar

17. The problem • Input • IPTV streaming: multicast & P2P • Random peers, preferred peers, locality optional: ρ • Watching TV: CHT, i, pi, vi • Network topology: lu, lm • Output • Average bandwidth utilization: B • Bandwidth of one stream: B0 Core mcast Core ucast Access Core Access up Access down Networking Seminar

18. Access downstream • The easy solution: • All U users watch a TV channel • The not so-easy solution • As an exercise: sum for all channels Does not depend on the channel popularity Networking Seminar

19. Access upstream • It depends only on the channels using P2P • g channels IP multicast / N – g channels using P2P Networking Seminar

20. Core unicast • Only for TV channels that use P2P • Depending on the average path length: lu • Locality? Ratio of viewers using the server Ratio of viewers using a peer P2P path length P2 server path length Networking Seminar

21. Core multicast • Only for TV channels that user IP multicast • Depending on the average tree size: lm Depends on the group size, i.e. channel popularity (number of viewers) Networking Seminar

22. Network Popularity Overlay Locality Putting everything together Networking Seminar

23. Let’s sum up • The bandwidth has 4 components • Multicast channels: access downstream & core multicast • Unicast channels: access down/up & core unicast • Intuitive result Bandwidth (B) Access down Access up Core ucast Core mcast Number of channels using multicast (g) Networking Seminar

24. Network effect • Two groups of channels: 20 popular & 80 unpopular • Choose g between 0 and 100 • For every network topology (m) Better connected network, less bandwidth Q1 = 0.6 Multicast is better, especially for non-popular channels Q2 = 0.4 Networking Seminar

25. Network effect • Same for 3 groups of channels • 20 very popular, 30 average, 50 unpopular Q1 = 0.4 Q2 = 0.3 Q3 = 0.3 Networking Seminar

26. Popularity effect • Increase the popularity of the popular channels • 20 popular channels, 80 unpopular channels Increasing popularity of popular channels Well well well… we don’t gain so much by using multicast Networking Seminar

27. Overlay effect • For unicast channels: use a peer or use the server? • Use a peer: scalable, distributed system • Use the server: centralized system • Let’s play with ρ Using the server, we cut the upstream in the access network Networking Seminar

28. Locality effect • Let’s pull the ace card for P2P: locality • P2P cannot cut from the access upstream: we need the upstream • P2P can cut from the distance between peers: the server is fixed! no locality locality What we loose in the upstream for 100% P2P we can gain with a locality factor of 0.8 Networking Seminar

29. Bandwidth vs. popularity • For one channel, we compare unicast and multicast • Changing the channel popularity • We have 10000 users, 100 channels: the average popularity is 100 users/channel Here: multicast These values are for a worse P2P case! Cold channels Hot channels Here: we can choose Networking Seminar

30. Simulation results Did we get the equations right? Networking Seminar

31. The software • Put everything in a computer simulation • Test the an actual P2P overlay • User behavior over time: channel holding time • Objectives • Verify our equations (whether the averages hold) • Verify our assumptions (can ρ describe the peer discovery decisions) • Determine realistic values for ρ and λ (locality) • We implemented the for P2P algorithms • Random peer, with and without locality • Preferred peer, with and without locality • Preferred peer: constraints on bandwidth, duration on the TV channel (less churn), distance from the server, etc. Networking Seminar

32. Simulation data Although the design of the P2P overlay may affect the locality factor we can obtain The equations approximate well the bandwidth utilization We ensure plenty resources(each peer can server at least 2 other peers): ρ can be high Networking Seminar

33. Summing up Networking Seminar

34. Multicast scalability • Scalability has been recognized and studied • There is no natural way of consolidating multicast entries • There are some solutions on aggregation but not uniformly implemented • We acknowledge that scalability is only a performance problem What we gain in terms of bandwidth What we loose in terms of scalability Networking Seminar

35. Is there room for P2P? • In current IPTV deployments there are many unpopular channels (few users per channel) • But their number is limited: hundreds • What happens for many more TV channels? • Third party service providers • User generated content • Of course, a definitive answer depends on… • Will the telcos leverage their set-top boxes for this services • Cost estimation (pricing is not difficult even for multicast alone) • We only examined bandwidth and scalability • Other considerations (delay) Networking Seminar

36. Thanks Networking Seminar