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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 Symposium pp. 2639-2644 101062643 范家賓 2013/6/18 1

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Outline Abstract Introduction The model Simulation results Further Work Conclusion 2

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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 3

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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 4

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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 5

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6 The model At moment t 0 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 timeline Enter the overlay network at t 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. Publishes the information and payoff it received to the common knowledge forever. Leaves the overlay network.

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7 The model The generation of payoff is modeled by two queueing systems M t : G t : ∞ : 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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8 The model General relations τ : the period of time from the users entering the system at t in to need to assess QoE The users which enter the system at t in publish their payoffs at t out 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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9 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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10 The model Metric for File-Sharing System Accesses the QoE and maps download times to payoffs We needs time τ to download a file, we assume that the payoff received is equal to – τ According to this metric Possible 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 t The pdf of these random variables The longer a user downloads a file, the lower is the payoff it receives.

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11 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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12 Simulation results The comparison of the model-based calculations and the simulation results

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13 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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14 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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15 Simulation results The stable-state value of the ALTO popularity depends more on β than α

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16 Further Work Not every user is aware of the possibility of optimization from t 0 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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17Conclusion 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.

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