AIMD fallacies and shortcomings

Prasad

1

AIMD claims:

Guess What !?

“Proposition 3. For both feasibility and optimal convergence to fairness, the increase policy should be additive and the decrease policy should be multiplicative.”

AIMD claim is untrue !

Consider the following simple example:

No. of users = 2

Init loads of users X1 = 17 and X2 = 0

Load goal, Xgoal = 20

Fairness goal, Fgoal = 99%

AIMD equations

Let aI = 1,aD = 0, bD = 0.01 and as per AIMD claim, bI should be 1

Fairness index is given by:

After plugging in all the values…

Result is (after 3 iterations):

Now, change bI to 1.1. In other words,

introduce a multiplicative-component during

increase. Result then is (after 3 iterations):

2

With AIMD, there is a possibility of unlimited overload after convergence

AIMD equations

After summing the values for n users we get,

Defining overload to be:

We getOverload =

The problem is, as n becomes large, overload becomes large as well !

3

AIMD is rather slow w.r.t convergence of efficiency

4

All issues mentioned till now have one thing in common – they are all related to the synchronous communication system

This model is too simple and unrealistic and hence, inferences made based on it may not hold at all in a real system

And Guess what !?

5This is the best part !

AIMD does not guarantee fairness !

(in a more realistic asynchronous communication system like the Internet)

A better model