1. Resource pricing and the evolution of congestion control By R. J. Gibbens and F. P. Kelly

2. A proportionally fair pricing. A fair distribution according to a price the user is willing to pay. Why ? How ?

3. Rates according to shadow pricing Let Then The change in the rate is:

4. Rates according to shadow pricing If w(t) = w r Then the stable point of the system is : A proportionally fair per unit charge.

5. Congestion Mechanisms Creating various measurements and congestion control algorithms in the network itself (routers). [floyd and fall] Creating incentives for the end nodes to use congestion control – charge aware TCP

6. Different approaches to charge aware TCP Paris metro pricing Smart market

7. The Expected Cost and Shadow price

8. The Expected Number of marks

9. When distribution is more general Thus

10. Congestion Algorithm 1 the Elastic User(w) Where

11. Congestion algorithm 2 File Transfer(F,W) Elastic User that changes the Payment.

12. Queue Marking Mechanism Problem: Packets that arrive early at the busy period leave without being marked Packets that arrive after loss may be marked (although their shadow path is 0).

13. Queue – Marking Mechanisms 1.When a packet is lost mark all the packets in the queue and mark additional number. 1(Variant) Mark every packet from the first loss to the time the queue become empty

14. Queue - Marking Mechanisms (2) 2. Mark with probability calculated from the history of the queue. 3. Mark when ever a smaller virtual queue loses packet.

15. Comparison with the Internet Packet conversation principle A new packet isn’t put into the network until the old packet leaves = self clocking

16. Solving the problems Slow-start – exponential increase to the window size – Increase with each ack received Congestion avoidance: 1. Additive increase. 2. Multiplicative decrease.

17. Current congestion algorithm disadvantages Not user specific. Dropping packages is an extreme mechanism for congestion control. The rate at which the signals a generated in the source.

18. Response of end-nodes to Congestion Jacobson – Average Rate Elastic user - Inverse proportion to

19. Jacobson Average Rate in our Equations If the user needs the average rate of Jacobson than the utility function would produce that rate.

20. Self Clocking in our Equations When no congestion indications are present File-transfer is doubling it’s rate (with proportion to T).

21. Self Clocking in our Equations Elastic User can be self clocking if cwnd increased by So the change in the rate is :

22. Game Theory Model If the user is price-aware he will maximize: The solution is When

23. Game Theory The average paying is When  r =  is constant and equal Then Conclusion – users shade their bids if they have market power

24. Concluding remarks By appropriately marking the resources end-nodes are provided with the necessary information to make efficient use of the network resources