Can Internet Video-on-Demand Be Profitable? SIGCOMM 2007 Cheng 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 traffic High-degree of locality Zipf 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

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 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.