Modeling Resource Sharing Dynamic of VoIP users Over a WLAN Using a Game-Theoretic Approach

1. Modeling Resource Sharing Dynamic of VoIP users Over a WLAN Using a Game-Theoretic Approach Presented by Jaebok Kim

2. Motivation

3. Motivation A great number of people using Internet for multimedia contents today Need resource sharing mechanism & congestion control for limited resource and competition Suggested many models & mechanism for success of multimedia contents services in Internet However, suggested models will not be applied widely as TCP was Why? 450 M users

4. Motivation Because of complexity of Internet in terms of anumber of applications, competing developers, users So, we can dare to say….

5. Motivation Network doesn’t implement resource sharing mechanism! Instead, where can we find this? Yes, end-user applications do it. called, end-user congestion control Only widely deployed multimedia congestion control Yes, it’s Skype, again.

6. Motivation So, resource sharing mechanism is delegated to end-users. What can they do for their quality? What if they choose their strategy freely?

9. Rising Questions • Is it bad or good for a network to allow users to have • that freedom? • What are the performance consequences of letting the users to freely choose their rates? • (how good? how bad?)

10. How to get the answers • Conducting experimental measurement to see performance consequence

11. Conflicts occur because of limited resource between users! There is no technical solution! Adopting this view, no technical solution! We’ll have real experiment letting users have freedom. It will show collapse or adaptation of users. That’s it? is it done? No, we need to explain the result. So, we’ll use a model Why?

12. Why do we use model? • To apply quantitative reasoning to observations • To discover aspect of reality or understand it • How do we usually start? • characterizing the system • assumptions about how it works • interpret these into equations & a simulation • validation: • So, to understand what if users have freedom to choose • strategy, we need a model. • What kind of model do we need?

13. Its goal is to understand interaction between players in competition. Let’s adopt Nash Equilibrium, one of its concepts. No player can benefit by changing strategy while the other players keep theirs unchanged It’s similar to that users converge eventually.

14. Two types of Game Theory A normative theory – to find what designers should do to solve a problem A descriptive theory – to understand reality and infer metrics (called behavioral game theory) Give us better understanding of the behaviors of users, competing for medium access in a WLAN.

15. How different from previous work Plus, wireless link

20. Transition Rate between two states based on measurement of voice quality Choosing high rate doesn’t mean high quality of voice! Ex) (0,0,6) Behavioral Model (MC) State: the vector of n of players adopting strategies (#users low rate,# users average rate, #users high rate) Ex) (1,2,3) , (0,6,0)… Active measurement(emulating the real measurement) using Traffic Generator & NS

21. The example of MC Let’s see the simple example. Only two users, two choices of rate (high, low) We’ll make a model looking like this. 0.3 0.6 0.7 0.4 0.6 (2, 0) (1, 1) (0, 2) 0.3 0.1

22. Injecting data, Computing statistics Transition Rate calculated from suggested MOS Estimating MOS (mean opinion score) :1~5 Using PSQA (Pseudo-Subjective Quality Assessment) Emulating the behavioral measurement with exact flow mapped to VivaVoz’s jitter, loss.. Giving flexibility for scenarios RNN learning algorithm ,better than manual measurement, using six parameters (loss rate…)

23. Overview of the model

24. We need data to parameterize the model. Emulating the behavioral measurement

25. Channel Performance by Active Measurement Emulating the behavioral measurement using Traffic Generator Swings around 160 kbps Sum of user’s rate

26. Now, we can parameterize the QoS model by using the data.

27. Sim & measurement are matched well. So, We can use A.M to parameterize our model.

28. The Top Layer: MC model State: the vector of n of players adopting strategies (#users low rate,# users average rate, #users high rate) changing to another depending on the quality of service Behavioral Model (MC) So far, we’ve got parameters for QoS model. What about parameters for MC model? Active measurement(emulating the real measurement) using Traffic Generator & NS

29. After change, quality becomes better than before or Better than tolerance level Transition Rates between states in MC N of users adopting L * PDF Try different rate Makes a mistake Worse than before T & ɣ are extra parameters suggested in the paper. We assume users can’t tolerate a certain poor quality. T: exploring rate ɣ: tolerance level We know most parameters calculated from QoS model, But, how do we decide ɣ? Let’s wait until we take a look around the real experiment. A vector of a measure of voice quality A measure voice quality, when any user adopts strategy l A measure of voice quality, after changing strategy from l to m

30. Example of transition in MC 0.8 ? ? ? ? ? (2, 0) (1, 1) (0, 2) S(#High, #Low), initial state is (2,0) How do we get the transition rate for this, when a user change strategy from high rate to low rate? S(2,0) ->S(1,1) First, we get the a vector of MOS for them from QoS model. Let’s assume that U(2,0) & U(3,3) for each state. U2 after the change is 3, it’s greater than U1, 2before the change. So, the transition rate is calculated from the following: S1 * = 2 * 0.4(assumed value) = 0.8 ? ?

31. So far, what have we done? So far, we’ve understood the structure of the game theory model & how it works. But after we see the result of real experiment, we’ll see the result pointed out by the model. By comparing them, we know whether the model is correct or not.

32. Free to choose rate, no bail out! Non Interactive: Pre-recorded voice Interactive: Freely talked

33. Share AP, Choose rate freely 3 pairs of users

34. Look at the final rates, selected at the end of experiment. We could see “auto-regulation ability”.

35. Tend to be more aggressive when using interactive application Without any technical solution, they converged to an efficient state (Using resources almost fully!) No collapse!

36. How to set tolerance level: r ? Aggregate rate varies from 130 to 160kbps In this range, MOS is greater than at least 2.6

38. N of All possible states = 28 Since 6 hosts & 3 rates Yes, the results of experiments and the model are so close. The model shows the adaptation of users.

40. short term: first transition medium term: frequently changing rates long term: reach N.E for the first time Super Long term: steady state P((0,5,1)->(0,6,0)) Without setting the tolerance level, the possibility of going to tragedy of the commons will be high. For simplicity tolerance level is set to zero. A way of initial state(0,5,1) to 28 possible states

42. End-user dynamic resource sharing mechanism is a big issue. We ask a question what happens if users are allowed to have freedom. We could see the result by conducting experimental measurement. We expanded game-theory model to explain the adaptation of users. We compared the experiment with the model. “Users could adapt to the network conditions and converged to efficient equilibrium.”

43. Contribution of this paper Extending the previously suggested model for behavioral experiments and getting the method to parameterize it Conducting the behavioral experiments and showing how to use the model in order to explain the results of the experiment.

44. Q&A Thank you

45. Users tried to utilize rate fully and later, began adaptation to share resources

47. With tolerance level 2.6 Start with high rates is worse to get the equilibrium. Tolerance level is set to 2.6 The probability of all possible initial states to specific final states.

49. Parameters

50. Training of RNN How to train RNN & use it? Using VivaVoz(conducting real tests) & NetEM(emulating the network conditions like loss rate) People count up to 20 as quickly as possible. Free talking about a picture and scenario Individual gives quality score. The results as MOS form database for RNN to train. After training, RNN can produce MOS results by using parameters automatically. Much more cost-effective than real manual measurement!