1. A Strategy for Implementing Smart Market Pricing Scheme on Diff-Serv Murat Yuksel and Shivkumar Kalyanaraman Rensselaer Polytechnic Institute, Troy, NY yuksem@rpi.edu, shivkuma@ecse.rpi.edu

2. Outline Literature development : congestion-sensitive pricing the Smart Market (SM) pricing scheme Adaptation of SM to diff-serv Simulation experiments Summary

3. Congestion-Sensitive Pricing Increase the price when congestion, decrease when no congestion. A way of controlling user’s traffic demand and hence, a way of controlling network congestion Better resource (bandwidth) allocation Fairness Problems: Users don’t like price fluctuations! Each price change must be fed back to the user before it could be applied, i.e. hard to implement in a wide area network.

4. The Smart Market (SM) Proposed by MacKie-Mason and Varian in 1993 A congestion-sensitive pricing scheme Price-per-packet reflecting congestion costs Users make auction by assigning a “bid” value to each packet before sending it into the network. The routers maintain a threshold (cutoff) value and pass the packets with bids larger than the threshold. They give priority to the packets with higher bid! The cutoff value changes dynamically based on local congestion

5. The Smart Market (SM) (cont’d) The price for each packet is the highest cutoff value it passed through, i.e. market-clearing price. Why is SM important? The first congestion-sensitive pricing scheme Designed for the smallest granularity level (i.e. packet) and hence, attempts the highest possible congestion-sensitivity for network pricing Ideal scheme from an economic perspective because of its pure congestion-sensitivity

6. Adaptation to Diff-Serv For data plane packets: Edge routers (ERs): write the bid value (b) to the packet header and then send the packet into the core Interior Routers (IRs): maintain a priority queue, sorted according to packets’ bids if b<T, drop the packet if b>=T, update the packet’s clearing-price field and forward it For control plane packets: ERs and IRs maintain a time interval (τ) which is greater than round-trip time (RTT) to operate. Hence, the customers are fed back with the current price and their account information at every τ.

7. Adaptation to Diff-Serv (cont’d) ERs and customers: Ingress-ER sends a “probe” packet to the network core at every τ to find out the current clearing-price of the network. Egress-ER responds to the probe packet by a “feedback” packet that includes current clearing- price and bill to the customer. set the bids of control packets to the maximum bid value (limitation-- bids must be bound to a range) Ingress-ER informs the customer about his bill and the current clearing-price. Customers adjust their bids and traffic based upon the bill, the clearing-price, and their utility. IRs: update the threshold (T) value at every τ update control packets’ clearing-price field too

8. Cutoff Value, T SM says that the IRs should adjust the cutoff value such that T = n/K * D’(Y), where n is the number of customers and K is the capacity of the network. IRs update T by calculating D’(Y) at the end of each interval, τ. We used the following approximation for calculating T: where D[i] is the average delay at interval i, and T[i] is the cutoff value for interval i.

9. Simulation Experiments Packet size is 1000bytes. Propagation delay is 0.1ms on bottleneck links and 10ms on the others. RTT is 24ms. The time interval τ is 1000ms. User utility is concave: u(x) = w log(x) Users have a budget w and maximize their surplus by sending at a rate w/p. We simulated two versions of SM: SM-SORTED: higher bids have priority at IRs SM-FIFO: first-come first served

10. Simulation Experiments (cont’d) 3 user flows with budgets 100, 75 and 25 $/Mb. Total simulation time is 3000s.

11. Simulation Experiments (cont’d)

13. To observe service differentiation: Two flows with a varying ratio of budgets.

14. Simulation Experiments (cont’d) Each user flow has a budget of 10$/Mb.

15. Simulation Experiments (cont’d)

16. Summary Major changes to SM are need for an implementation on diff-serv By extensive simulation we observed that: SM can control congestion with low queues and high utilization Packet sorting (i.e. priority to higher bids) degrades system performance SM performs in between max-min and proportional fairness