An Analysis of Chaining Protocols for Video-on-DemandJ.-F. PârisUniversity of Houston

Thomas Schwarz, S. J.Universidad Católica del Uruguay

Introduction

Video-on-demand lets Different customers watch Different videos atDifferent times

Very high bandwidth requirements

Solutions (I)

Distributing server workload among several sitesContent-delivery networksLocal caches, …

Letting the server broadcast same video data to all customers watching the same videoNot possible on today's Internet

Solutions (II)

Let customers participate in the video distributionP2P solutionAvailable distribution bandwidth grows

linearly with the demandCheap and easy to deployRequires everyone to cooperate

• Must penalize selfish customers

Chaining

One of the oldest VOD solutionsS. Sheu, K. A. Hua, and W.

Tavanapong. Chaining: A Generalized Batching Technique for Video-on-Demand Systems. Proc. ICMS Conference, June 1997.

Involves clients in video distribution process

Assumptions

Customers have enough upstream bandwidth to forward the video to the next client

Customer buffer sizes do not allow them to store entire videosCan only store last β minutes

• A reasonable assumption in 1997

Basic chaining

Customer requests form a chainFirst customer in the chain receives its

data from the serverSubsequent customers receive their

data from their immediate predecessor Chain is broken each time two

consecutive requests are more than β minutes apart

An example

Customer A

Customer B

Customer C

Stream from server

Stream from customer A

Stream from server

Expanded chaining

Assumes thatCustomers have enough buffer space

to cache the whole contents of the video• Helps with rewind command

Customers will disconnect once they have finished playing the video• A realistic assumption

How it works

From serverCustomer A

From ACustomer B

From BCustomer C

To A To B To CSERVER

t

t’

From server

From server

Server bandwidth requirements (2-hour video)

0

1

2

3

4

5

6

7

8

1 10 100 1000Requests/hour

Ser

ver

ban

dw

idth

(ch

ann

els)

Expanded chaining

Chaining with a 6 minute buffer

Chaining with a 15 minute buffer

Chaining with a 30 minute buffer

Chaining with a one-hour buffer

Accelerated chaining

Has clients forward their video data to the next client in the chain at a slightly higher rate than the video consumptionAcceleration factor will vary between

1.01 and 1.1

How it works

From serverCustomer A

From ACustomer B

From BCustomer C

To A ToB To CSERVER

t

t’

From server

From server

Server bandwidth requirements (2-hour video)

0

0.2

0.4

0.6

0.8

1

1.2

1 10 100 1000

Requests/hour

Ser

ver

ban

dw

idth

(ch

ann

els)

Expanded chaining

1% acceleration

2% acceleration

5% acceleration

10% acceleration

Motivation for further work

All these results were obtained through discrete simulation Mere numerical values

Could we not use analytical methods?Would get algebraic solutionsCould derive maxima/minima

Our assumptions

D is video duration β is buffer size λ is customer arrival rate f is video acceleration rate Time between arrivals is governed by

the exponential distribution with probability density function

p(t) = λ e-λt

Basic chaining (I)

Two cases to considerInterarrival time is less than β

• Previous customer forwards the video• No server workload

Interarrival time is more than β• Server transmits whole video

Basic chaining (II)

Average server workload per video is

Average server bandwidth is

De

dteDdtew tt.00

DewB

Expanded chaining (I)

Two cases to considerInterarrival time Δt is less than D

• Previous customer forwards part of the video (D – Δt)

• Server transmits remaining part (Δt)

Interarrival time Δt is more than D• Server transmits whole video

Expanded chaining (II)

Customer A

From AFirst case:Customer B

Second case:Customer C

t

t’ > D

From server

From server

Customer A

Expanded chaining (III)

Average server workload per video is

Average server bandwidth is

)1(1

0

D

t

D

D t

e

dteDdtetw

DewB 1

Accelerated chaining (I)

Two cases to considerInterarrival time Δt is less than D

• Previous customer forwards part of the video:

min(D, f (D – Δt))

• Server transmits remaining part Interarrival time Δt is more than D

• Server transmits whole video

Accelerated chaining (II)

Result is a fairly complicated expression

with ρ = 1/f

Comparing analytical results with simulation results (I)

0

1

2

3

4

5

6

7

8

1 10 100 1000

Requests/hour

Ser

ver

ban

dw

idth

(ch

ann

els)

Expanded chaining

Chaining with a 6 minute buffer

Chaining with a 15 minute buffer

Chaining with a 30 minute buffer

Chaining with a one-hour buffer

Comparing analytical results with simulation results (II)

0

0.2

0.4

0.6

0.8

1

1.2

1 10 100 1000

Requests/hour

Ser

ver

ban

dw

idth

(ch

ann

els)

Expanded chaining

One percent faster

Two percent faster

Five percent faster

Ten percent faster

Conclusion

Very good agreement between analytical and simulation resultsTwo techniques validate each other

Analytical results provide a better investigation tool than simulation resultsCan compute bandwidth maxima, …

Future work

Add an incentive mechanismTo penalize freeloaders

Investigate how mechanism interacts with protocol

Implement fast forward/jump

Develop a test bed implementation

Handling early termination:Original schedule

From serverFrom serverCustomer A

From ACustomer B

From BCustomer C

To A To B To CSERVER

t

t’

From server

Handling early termination:After customer B leaves

From serverCustomer A

Already playedCustomer B

Customer C

To A To CSERVER

t

t’

From server

Was from B

From A