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Modeling Resource Sharing Dynamic of VoIP users Over a WLAN Using a Game-Theoretic Approach. Presented by Jaebok Kim. Motivation. Motivation. A great number of people using Internet for multimedia contents today Need resource sharing mechanism & congestion control for - PowerPoint PPT Presentation
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Modeling Resource Sharing Dynamic of VoIP usersOver 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 & mechanismfor 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 controlOnly 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 havethat 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 per-formance 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 modelWhy?
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 worksinterpret these into equations & a simulationvalidation:
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 competi-tion.
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.
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.
How different from previous work
Plus, wireless link
Active measurement(emulating the real measurement) using Traffic Generator & NS
Behavioral Model (MC)
Transition Rate be-tween two states based on measure-ment of voice quality
State: the vector of n of players adopting strate-gies(#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)0.4 0.6
0.30.1
0.70.6 0.3
Injecting data,Computing statistics
Giving flexibility for sce-narios
Estimating MOS(mean opinion score)
:1~5
Using PSQA(Pseudo-Subjective Quality
Assessment)
Transition Rate calculated from suggested MOS
RNNlearning algorithm
,better than manual mea-surement,
using six parameters(loss rate…)
Emulating the behavioral measurementwith 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 measurementusing Traffic Generator
Channel Performance by Active Mea-surement
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 mistakeWorse than before
Try different rate
After change, quality becomes better than before orBetter than toler-ance level
A vector of a measure of voice quality A measure voice quality, when any user adopts strategy lA 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 levelWe 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.U2 after the change is 3, it’s greater than U1, 2 before the change. So, the transition rate is calculated from the following:S1 * = 2 * 0.4(assumed value) = 0.8
0.8
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 voiceInteractive: 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 160kbpsIn 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 = 28Since 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 measure-ment.
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 ex-periments 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 experi-ment.
Contribution of this paper
Thank you
Q&A
Users tried to utilize rate fullyand later, began adaptation to share resources
With tolerance level 2.6
Tolerance level is set to 2.6The 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