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

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? Motivation 450 M users

Because of complexity of Internet in terms of a number of applications, competing developers, users So, we can dare to say…. 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 Motivation Yes, it’s Skype, again.

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

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?) Rising Questions

How to get the answers Conducting experimental measurement to see performance consequence

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?

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

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 player s keep theirs unchanged It’s similar to that users converge eventually.

Two types of Game Theory A normative theory – to find what designers should do to solve a p roblem 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.

How different from previous work Plus, wireless link

Active measurement(emulating the real measurement) using Traffic Generator & NS Behavioral Model (MC) Transition Rate between two states based on measurement of voice quality 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)… Choosing high rate doesn’t mean high quality of voice! Ex) (0,0,6)

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. (2, 0)(0, 2)(1, 1)

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

Overview of the model

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

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

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

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

Active measurement(emulating the real measurement) using Traffic Generator & NS Behavioral Model (MC) 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 So far, we’ve got parameters for QoS model. What about parameters for MC model? The Top Layer: MC model

N of users adopting L * PDF Makes a mistake Worse than before Try different rate After change, quality becomes better than before or Better than tolerance level 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 Transition Rates between states in MC 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.

Example of transition in MC (2, 0)(0, 2)(1, 1) ?? ? ? ?? ? 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. U 2 after the change is 3, it’s greater than U 1, 2 before the change. So, the transition rate is calculated from the following: S 1 * = 2 * 0.4(assumed value) =

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. So far, what have we done?

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

Share AP, Choose rate freely 3 pairs of users

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

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

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

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

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

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.”

1)Extending the previously suggested model for behavioral experiments and getting the method to parameterize it 2)Conducting the behavioral experiments and showing how to use the model in order to explain the results of the experiment. Contribution of this paper

Thank you Q&A

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

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

Parameters

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! Training of RNN

Output of training