A Strategy for Implementing Smart

Market Pricing Scheme on Diff-Serv

Murat Yuksel and Shivkumar Kalyanaraman

Rensselaer Polytechnic Institute, Troy, NYyuksem@rpi.edu, shivkuma@ecse.rpi.edu

Outline

Literature development : congestion-sensitive pricing the Smart Market (SM) pricing

scheme Adaptation of SM to diff-serv Simulation experiments Summary

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.

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

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

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.

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

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.

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

Simulation Experiments (cont’d)

15Mb

15Mb

10Mb

0

1

15Mb

15Mb 1

2

flow 2

15Mb

0

15Mb

2

A B

flow 1flow 0

Single-Bottleneck

3 user flows with budgets 100, 75 and 25 $/Mb.

Total simulation time is 3000s.

Simulation Experiments (cont’d)

Simulation Experiments (cont’d)

Simulation Experiments (cont’d)

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

Simulation Experiments (cont’d)

15Mb

15Mb 10Mb

15Mb

A

3

0

1

15Mb 010Mb D10Mb

15Mb

15Mb

15Mb

15Mb

1 2

CB

32

flow 3

flow 0

flow 1 flow 2

Multi-Bottleneck

Each user flow has a budget of 10$/Mb.

Simulation Experiments (cont’d)

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