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Will P2P Users Cooperate with ISPs?

A Word-of-Mouth Communication Approach

Piotr Wydrych (AGH University of Science and Technology, Poland); Piotr Cholda

(AGH University of Science and Technology, Poland)

IEEE ICC 2012 - Next-Generation Networking Symposiumpp. 2639-2644

101062643 范家賓2013/6/18

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OutlineAbstractIntroductionThe modelSimulation resultsFurther WorkConclusion

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Abstract The problem of application-level traffic

optimization(ALTO)◦ (a) The case that before the option to cooperate with ISP

and optimize the service is enabled◦ (b) The case in which all clients try to optimize the traffic.

In this paper consider:◦ User may not wish to cooperate with the ISP◦ User does not perceive the optimization possibility to be

valuable enough to cooperate with ISP

Analytical model:Calculating the time-dependent value of the predicted popularity of the cooperate-to-optimize option

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Introduction P2P

◦ ISPs gained massively broadband access◦ Uncontrolled costly inter-domain flows

Focused on proposing methods to decrease P2P flows that cross Autonomous Systems(AS) boundaries.

Cooperate between users of P2P and ISPs 1. “Can ISPs and P2P users cooperate for improved performance?”, V.

Aggarwal, A. Feldmann, and C.Scheideler

2. “P4P: Provider portal for applications”, H. Xie, Y. R. Yang, A. Krishnamurthy

◦ Compare two case: with optimization service being either fully off or totally on.

◦ Assume: all users either willing to cooperate with their ISPs or not

ALTO: application-layer traffic optimization

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Knowledge about new Internet options◦ Internet forums, blogs, chats ….◦ Information shared among users is called the common

knowledge◦ Word-of-mouth marketing

Choose between two opitons then providing different payoffs◦ (a). Use the unmodified P2P application and encounter

plausible traffic(and quality) suboptimalities◦ (b). Cooperate with ISPs and optimize the traffic

generated by the P2P application

Develop a model to predict the level of the user-ISP cooperation.◦ Ellison and Fudenberg, ”Word-of-mouth communication

and social learning”

Introduction

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The model At moment t0 the ALTO service is introduced and ISPs ask end-

users to cooperate with them. • Three group:

(a). Always cooperating : cooperative users (probability: α) (b). Always non-cooperating : non-cooperative users ( probability: β )

(c). Cooperate if see cooperative users receive better payoffs

than who do not cooperate: rational user (probability: 1-α-β )

Peer’s actions on a timeline① Enter the overlay network at t

② Probability x(t) decides to cooperate, 1-x(t) decides not to cooperate.

x(t) depends on the N payoffs samples held in the common knowledge on previous state of network.

③ Publishes the information and payoff it received to the common knowledge forever.

④ Leaves the overlay network.

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The model The generation of payoff is modeled by two

queueing systemsMt :

Gt :

∞ : The number of servers

General relations

Cooperating user arrive with the rate

Non-cooperating user arrive with the rate

The Poisson process arrival rate is modulated by the ALTO popularity The time needed to assess QoE may vary during the whole modeled period

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The model General relations

τ: the period of time from the users entering the system at tin to need to assess QoE

• The users which enter the system at tin publish their payoffs at tout with the rate

• The intensities of the process of arrival of the payoff samples to the common knowledge at t is characterized by

• The intensities of the arrival of the payoff samples perceived by a deciding user are given as T: time constant

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The model :The probability of selecting a payoff sample generated by a

cooperating user from the common knowledge

The average properties of he random variables

1≦ k ＜ N payoff samples where generated by cooperating users from the common knowledge The probability that a rational user would cooperate: ( binomial distribution B(N,

)).

The probability that a user will decide ( at moment t ) to cooperate

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The model Metric for File-Sharing System

Accesses the QoE and maps download times to payoffsWe needs time τ to download a file, we assume that the payoff received

is equal to –τ

According to this metricPossible to calculate the probability density

function(pdf) of random variables describing the payoff samples arriving to the common knowledge

Payoff sample is generated at moment t : User finished downloading a file

at t The probability that a payoff publishes at t and equal to –τ is equal to the

probability that a user started downloading at t –τ and finished at tThe pdf of these random variables

The longer a user downloads a file, the lower is the payoff it receives.

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Simulation results The topology used in the simulations

Perform in the Erouption.S BitTorrent network simulator

The ALTO service was deployed in all stub ASes.

After 10 hours to warm-up, the overlay network is stable and the common knowledge contained a sufficient number of payoffs samples, the ALTO service was started.

N samples were randomized from the common knowledge using the weighted sample. The weight of each payoff sample was equal to , T = one hour

Both α and β were set to 5%

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Simulation results The comparison of the model-based calculations and the

simulation results

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Simulation results Reaction to changes of parameter values

The time constant T determines how fast ALTO popularity grows to its maximum and how fast the system converged to the stable state

The stable-stae value of the ALTO popularity does not depend on the time constant T

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Simulation results

The larger the file, the bigger was the ratio of download times for cooperating and non-cooperating users.

The larger the file, the more beneficial it was for users to cooperate and the higher was the stable-state value of the ALTO popularity

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Simulation results

The stable-state value of the ALTO popularity depends more on β than α

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Further Work Not every user is aware of the possibility of

optimization from t0

α should be varied over time.Provides a new service to its clients, it starts an advertising campaign to get the clients interested in the service.

A user not only at its start to make decision.

makes a decision periodically

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Conclusion By using a word-of-mouth communication model

to calculate the popularity, if the users can be really interested in the ALTO-related cooperation,

Will P2P Users Cooperate with ISPs?

The output is not only an the optimistic fact, but also finding some general rules of the thumb(1). The share of cooperating users will be quite high and not

dependent on the time horizon.

(2). The more a user exploits P2P system, the easier it is to convince such a client to the cooperation.

(3). In a long-run the only users who will not cooperate are the ones that are generally unwilling to cooperate; the rational or positive user will be convinced by performance improvement at last.