Simulating the Smart Market
Pricing Scheme on DifferentiatedServices Architecture
Murat Yuksel and Shivkumar Kalyanaraman
Rensselaer Polytechnic Institute, Troy, NY
yuksem@cs.rpi.edu, shivkuma@ecse.rpi.edu
CNDS 2001, Phoenix, AZ
Outline
• Literature development :
– Internet pricing
– congestion-sensitive pricing
– the smart market pricing scheme
•
•
•
•
•
Issues on deploying the smart market on diff-serv
Adaptation of the smart market to diff-serv
Packet-based simulation of the smart market
Simulation experiments
Summary and future work
CNDS 2001, Phoenix, AZ
Three Basic Pricing Strategies
• Flat-rate pricing:
– fixed price for a time period, F where F>0
• Usage-based pricing:
– plus fixed price for unit amount of traffic, F+T where
F>=0 and T>0
• Congestion-sensitive pricing:
– plus varying price based upon congestion level of the
network, F+T+C where F>=0, T>=0 and C>=0
CNDS 2001, Phoenix, AZ
Congestion-Sensitive Pricing
• A way of controlling user’s traffic demand and hence, a
way of controlling network congestion
• Better resource (bandwidth) allocation
• Fairness
• The only possible way of achieving network and economic
efficiency simultaneously
• Level of congestion-sensitivity: The more opportunity for
price variations, the more opportunity for employing
congestion-sensitivity on the price.
• 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.
CNDS 2001, Phoenix, AZ
The Smart Market
• Proposed by MacKie-Mason and Varian in 1993 as a possible
congestion-sensitive pricing scheme for the Internet.
• Imposes a price-per-packet that reflects incremental congestion
costs.
• Users make auction by assigning a “bid” value to each packet
before sending into the network.
• The routers maintain a threshold (cutoff) value and pass only the
packets with enough bid value. They give priority to the packets
with higher bid!
• The cutoff values changes dynamically according to the level of
congestion at that router.
• The price for each packet is the highest cutoff value it passed
through, i.e. market-clearing price.
CNDS 2001, Phoenix, AZ
The Smart Market (cont’d)
• Why is the smart market important?
– The first congestion-sensitive pricing scheme
– Designed for the smallest granularity level (i.e. packet or even
possibly bits) and hence, represents the highest possible
congestion-sensitivity for a network
– Ideal scheme from an economic perspective because of its pure
congestion-sensitivity
• FOCUS:
– To what extent the smart market is deployable, especially on diffserv architecture?
– How to adapt it to diff-serv?
– Can we develop a packet-based simulation of the smart market?
CNDS 2001, Phoenix, AZ
Deployment on Diff-Serv?
Customer
(sender)
Edge
Router
(Ingress)
Interior
Router
Edge
Router
(Egress)
Customer
(receiver)
• What is diff-serv?
– A standard architecture for the Internet:
• complex operations at network edges (i.e. edge routers (ERs))
• simple operations in network core (i.e. interior routers (IRs))
• Expected to be choice of ISPs and bandwidth providers
• Protocols for Service Level Agreement (SLA) are already
available
• Possible to make congestion-based pricing at the edges
CNDS 2001, Phoenix, AZ
Deployment on Diff-Serv? (cont’d)
• Too much theoretically defined. Assumes immediate
communication of the clearing-price, that is impossible in a
wide area network.
• No guaranteed service because packets can be dropped
based upon their bids.
• Packet reordering at the core is required.
• Bidding can be done at the edges, but clearing has to be
done in the core.
• Sensitivity and compatibility of parameters in the
formulas.
CNDS 2001, Phoenix, AZ
Adaptation to Diff-Serv
• For data plane packets:
– ERs:
• write the bid value (b) to the packet header
• and then send the packet into the core
– 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.
CNDS 2001, Phoenix, AZ
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 his budget.
– IRs:
• update the threshold (T) value at every τ
• update control packets’ clearing-price field too
CNDS 2001, Phoenix, AZ
Packet-Based Simulation
• Issues:
– What must be the customer model?
– How to set the cutoff value (T) at IRs?
– How to handle parameter sensitivity and
compatibility?
CNDS 2001, Phoenix, AZ
Customer Model
• Smart market says that each customer should
maximize {u(x) - D(Y) - px} or {u(x,D) - px} with
respect to x, where
–
–
–
–
–
x is the number of packets to send
u() is the utility of the customer
Y is the utilization of the network
D is the delay experienced by the customer
p is the current clearing-price of a packet for the
network
• Smart market also says that the value of x for
maximization can be found by equating the
clearing-price to marginal utility,
i.e. p = ∂u(x,D) / ∂x
CNDS 2001, Phoenix, AZ
Customer Model (cont’d)
• So, what is an accurate utility function for the
customer?
– model for the indifference curves between x and D:
x = (aD + b)^2, where a and b are constants
– utility function:
u(x,D) = x^(1/2) – aD
– p = u’(x,D) = 1 / 2x^(1/2)
– x = 1 / 4p^2
 number of packets to send in
the next interval!
• If customer’s budget is not enough for that value
of x, then she/he lowers it to x = Budget / p.
CNDS 2001, Phoenix, AZ
Cutoff Value, T
• Smart market 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.
• We assumed n/K to be constant for simplicity.
• IRs update T by calculating D’(Y) at the end of each interval, τ.
• IRs maps T values to [0,1], and hence loose accuracy…
Cutoff Value
1
0.8
0.6
Experimental T
0.4
Ideal T
0.2
0
5 10 15 20 25 30 35 40 45 50
Budget of the customer
Steady state cutoff value, T, for different customer budgets
CNDS 2001, Phoenix, AZ
Simulation Experiments
ER
Customers
(Senders)
...
ER
ER
IR
Bottleneck
IR
...
Customers
(Receivers)
ER
Configuration of the experimental network
•
•
•
•
Customers send CBR UDP traffic through their corresponding ERs.
Packet size is 1000bytes.
Bottleneck capacity is 1Mbps and propagation delay is 10ms.
All other links are with 100Mbps capacity and 1ms of propagation
delay.
• RTT is 24ms.
• The time interval τ is 0.4s = 400ms.
CNDS 2001, Phoenix, AZ
Simulation Experiments (cont’d)
Expe rime nt Numbe r of
Budge ts of
Total
Numbe r
C ustome rs the C ustome rs Budge ts
1
2
10,10
20
2
2
10,20
30
3
4
1,3,5,7
16
4
5
1,3,5,7,9
25
List of the experiments
• Network efficiency:
– bottleneck queue length
– bottleneck utilization
– packet drop rate
• Economic efficiency:
– volume (rate) allocations to customers
– steady-state cutoff value
CNDS 2001, Phoenix, AZ
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Bottleneck
Utilization
44.4
40.4
36.4
32.4
28.4
24.4
20.4
16.4
12.4
8.4
4.4
Cutoff Value
0.4
Utilization and Cutoff Value
Simulation Experiments (cont’d)
Simulation Time (secs)
Bottleneck utilization and cutoff in Experiment 1.
CNDS 2001, Phoenix, AZ
60
50
40
Bottleneck Queue
Length
30
20
10
43.6
38.8
34
29.2
24.4
19.6
14.8
10
5.2
0
0.4
Bottleneck Queue Length
(packets)
Simulation Experiments (cont’d)
Simulation Time (secs)
Bottleneck queue length in Experiment 1
CNDS 2001, Phoenix, AZ
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Bottleneck
Utilization
44.4
40.4
36.4
32.4
28.4
24.4
20.4
16.4
12.4
8.4
4.4
Cutoff Value
0.4
Utilization and Cutoff Value
Simulation Experiments (cont’d)
Simulation Time (secs)
Bottleneck utilization and cutoff in Experiment 4
CNDS 2001, Phoenix, AZ
60
50
40
Bottleneck Queue
Length
30
20
10
43.6
38.8
34
29.2
24.4
19.6
14.8
10
5.2
0
0.4
Bottleneck Queue Length
(packets)
Simulation Experiments (cont’d)
Simulation Time (secs)
Bottleneck queue length in Experiment 4
CNDS 2001, Phoenix, AZ
0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
Customer 1
40.1
36.5
32.9
29.3
25.7
22.1
18.5
14.9
11.3
7.71
4.11
Customer 2
0.52
Allocated Volume (Mbps)
Simulation Experiments (cont’d)
Simulation Time (secs)
Volume allocations to customers Experiment 1
CNDS 2001, Phoenix, AZ
0.1
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
Customer 1
Customer 2
Customer 3
Customer 4
40.1
36.5
32.9
29.3
25.7
22.1
18.5
14.9
11.3
7.71
4.11
Customer 5
0.52
Allocated Volume (Mbps)
Simulation Experiments (cont’d)
Simulation Time (secs)
Volume allocations to customers Experiment 4
CNDS 2001, Phoenix, AZ
Summary
• We proposed some major changes to implement the smart
market on diff-serv with UDP flows.
• We developed a simulator for the smart market comparable
to simulators of possible new pricing schemes for the
Internet.
• We observed that:
– the smart market meets all economic efficiency goals by pricing the
bandwidth accurately and allocating the bottleneck volume to the
customers proportional to their budgets.
– but it fails to fully meet network efficiency goals, because it cannot
utilize the bottleneck very well, although it is able to control
congestion with low bottleneck queue length and drop rate.
CNDS 2001, Phoenix, AZ
Future Work
• a thorough investigation of difficulties
implementing the smart market on TCP flows
in
• consideration of multiple diff-serv domain case
• the smart market’s behavior on bursty traffic
patterns
CNDS 2001, Phoenix, AZ