AIMD fallacies and shortcomings

1. AIMD fallacies and shortcomings Prasad

2. 1

3. 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.”

4. 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%

5. AIMD equations Let aI = 1,aD = 0, bD = 0.01 and as per AIMD claim, bI should be 1 Fairness index is given by:

6. 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):

7. 2

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

9. AIMD equations After summing the values for n users we get,

10. Defining overload to be: We get Overload = The problem is, as n becomes large, overload becomes large as well !

12. 3

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

14. 4

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

17. 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 !?

18. 5 This is the best part !

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

20. A better model