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 X 1 = 17 and X 2 = 0 Load goal, X goal = 20 Fairness goal, F goal = 99%

AIMD equations Let a I = 1,a D = 0, b D = 0.01 and as per AIMD claim, b I 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 get Overload = 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 !?

5 This is the best part !

AIMD does not guarantee fairness ! (in a more realistic asynchronous communication system like the Internet)

A better model