Can Internet Video-on-Demand Be Profitable?

SIGCOMM 2007Cheng Huang (Microsoft Research),

Jin Li (Microsoft Research),Keith W. Ross (Polytechnic University)

Presenter: Junction

Outline

• Motivation• Implementation• Characteristic of a Large Scale VoD Service

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.

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

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…)

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)

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 trafficHigh-degree of localityZipf distribution with flat

User Demand and Upload Resources• Estimate the upload bandwidth of a user by download

bandwidths.

Distribution of user download bandwidths Aggregate user demand and upload resources (April 18)

User bandwidth breakdown (KBPS)

Peer-assisted VoD might perform well

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

Service Evolution• Service quality upgrade and more users

Quality Evolution

Traffic Evolution

95 Percentile Rule• ISP charges the service provider each month according the

service provider’s peak bandwidth usage.

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)

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

mw

mp

mp

mmwp

mm p

MD

mmwS

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

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

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.

Simulation Result• Lower bound : a peer can feed content to any peer, not just

to the peers that arrived after it.

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

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.

Impact of User Interactivity• Conservative approach & optimistic approach

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

All Things Considered• Popularity

• Cost

scalability

The Impact of P2P on Internet Server Providers - ISP

• Relationship between ISPs– Transit, sibling, peering– Majority of P2P traffic is crossing entity boundaries

ISP-friendly peer-assisted VoD

• Fewer peers, more difficult More than 50% savings

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.