Can Internet Video-on-Demand Be Profitable?

1. Can Internet Video-on-Demand Be Profitable? SIGCOMM 2007 Cheng Huang (Microsoft Research), Jin Li (Microsoft Research), Keith W. Ross (Polytechnic University) Presenter: Junction

2. Outline • Motivation • Implementation • Characteristic of a Large Scale VoD Service

3. Motivation • VoD such as YouTube, MSN Video, Google Video, Yahoo Video, CNN… • As the trend of increasing demands on such services and higher-quality videos, it becomes a costly service to provide. • Using Peer-assisted to replace Server-Client : • By reducing the server’s bandwidth to reduce the cost that providers pay to ISPs.

4. Implementation • Using a nine-month trace from a client-server VoD deployment for MSN Video to gain some observation • Present a theory for peer-assisted VoD • Simulation • Impact of peer-assisted VoD on the cross-traffic among ISPs

5. Characteristics of a Large Scale VoD Service • Data Collection: • 2006 April to December: MSN Video service • Client-server mode • Covering over 520 million streaming requests for more than 59,000 videos. • Trace Records • Client Information Fields (ID, IP address, version…) • Video Content Fields (length, size, bitrate) • Streaming Field (connection, last, interactive…)

6. Identifying Users and Streaming Sessions • ID-identified (7%) & hash-identified player • different hashes come from different players • Streaming session : • A series of streaming requests from the same player to the same video file. (471/520)

7. Video Popularity Distribution • The greater the locality of requests to a subset of the videos, the greater the potential benefit for peer-assisted streaming. Similar regardless of traffic High-degree of locality Zipf distribution with flat

8. User Demand and Upload Resources Peer-assisted VoD might perform well • Estimate the upload bandwidth of a user by download bandwidths. Aggregate user demand and upload resources (April 18) Distribution of user download bandwidths User bandwidth breakdown (KBPS)

9. User Interactivity • View larger fraction of short videos • A large fraction of the users view videos without interactivity (> 60%) • It’s important to understand this interactivity while considering peer-assisted solutions for VoD. No interactivity does better

10. Service Evolution • Service quality upgrade and more users Quality Evolution Traffic Evolution

11. 95 Percentile Rule • ISP charges the service provider each month according the service provider’s peak bandwidth usage.

12. Theory of Peer-Assisted VoD • Single video & multiple video approach • Single : only redistributes the video currently watching • Multiple : redistribute a video previously viewed • Three basic operation modes • Surplus mode (S>D) • Balanced mode (S~=D) • Deficit mode (S<D)

13. Theory of three modes • Video rate : γ bps • M user types • : upload link bandwidth of a type m user • λ : the parameter of Poisson process to describe Users arrival • : the probability that an arrival is a type m user compound Poisson process m user types arrive as independent Poisson processes with parameters λ • : The average upload bandwidth of an arriving user • σ : a user’s expected sojourn time in the system Little’s law the expected # type users in the system is in steady state : the average demand is the average supply is

14. No-Prefetching Policy • Each user downloads content at the playback rate and doesn’t prefetch content for future needs. • For n = 1, we have s(u1) = r. • For n = 2, we have s(u1, u2) = r + max(0, r-u1). • So on …. (w1= 768 kbps, w2 = 256 kbps, γ = 512 kbps, σ = 300s) If (r-u1)<0, still upload r

15. Bandwidth Allocation Policies for Prefetching • Surplus upload capacity used to distribute • future content • creating a reservoir of prefetched content • exploited when the system shifts into a deficit state. • Operate better in the balanced mode. • Water-leveling & greedy

16. Water-leveling & Greedy • Ranking by the arrival order • determining required server rate • Allocate and adjust the growth rates • the growth rate of user k+1 doesn’t exceed user k @ data demands imposed on the server usually generated by oldest • Greedy : each user simply dedicates is remaining upload bandwidth to the next user right after itself.

17. Simulation Result • Lower bound : a peer can feed content to any peer, not just to the peers that arrived after it.

18. Real-World Case Study • Three cases: • All users watch the entire video • With early departures • With both early departures and user interactivity • Trace Analysis for the Two Most Popular Videos (case 1) • Typically no server resource are needed • Valleys • Flash crowd / long-lasting

19. Impact of Early Departures • Drive the system from the surplus mode, through the balanced mode, to the deficit mode by scaling the video bitrate. • Even with early departures, peer-assistance can provide a dramatic improvement in performance.

20. Impact of User Interactivity • Conservative approach & optimistic approach

21. All things Considered • Client-server, P2P, P2P with 3 times quality

22. All Things Considered scalability • Popularity • Cost

23. The Impact of P2P on Internet Server Providers - ISP • Relationship between ISPs • Transit, sibling, peering • Majority of P2P traffic is crossing entity boundaries

24. ISP-friendly peer-assisted VoD More than 50% savings • Fewer peers, more difficult

25. Conclusion • From the provider’s view • Server’s bandwidth actually reduced but how about the how traffic in ISP or even between ISPs • ISPs share their sibling and peering information to realize the truly ISP-friendly peer-assisted VoD.