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Simulating the Smart Market Pricing Scheme on Differentiated-Services Architecture. Murat Yuksel and Shivkumar Kalyanaraman Rensselaer Polytechnic Institute, Troy, NY yuksem@cs.rpi.edu, shivkuma@ecse.rpi.edu. Outline. Literature development : Internet pricing congestion-sensitive pricing - PowerPoint PPT Presentation
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CNDS 2001, Phoenix, AZ
Simulating the Smart Market Pricing Scheme on Differentiated-
Services Architecture
Murat Yuksel and Shivkumar KalyanaramanRensselaer 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 diff-
serv 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?
• 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
Customer(sender)
Customer(receiver)
EdgeRouter
(Ingress)
EdgeRouter
(Egress)
InteriorRouter
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…
Steady state cutoff value, T, for different customer budgets
0
0.2
0.4
0.6
0.8
1
5 10 15 20 25 30 35 40 45 50
Budget of the customer
Cut
off V
alue
Experimental T
Ideal T
CNDS 2001, Phoenix, AZ
Simulation Experiments
• 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.
Configuration of the experimental network
Customers(Senders)
Bottleneck... Customers(Receivers)...
ER
ER ER
ER
IR IR
CNDS 2001, Phoenix, AZ
Simulation Experiments (cont’d)
• Network efficiency: – bottleneck queue length
– bottleneck utilization
– packet drop rate
• Economic efficiency:– volume (rate) allocations to customers
– steady-state cutoff value
List of the experiments
Experiment Number of Budgets of TotalNumber Customers the Customers Budgets
1 2 10,10 202 2 10,20 303 4 1,3,5,7 164 5 1,3,5,7,9 25
CNDS 2001, Phoenix, AZ
Simulation Experiments (cont’d)
Bottleneck utilization and cutoff in Experiment 1.
00.10.20.30.40.50.60.70.80.9
1
0.4
4.4
8.4
12.4
16.4
20.4
24.4
28.4
32.4
36.4
40.4
44.4
Simulation Time (secs)
Uti
liza
tio
n a
nd
Cu
toff
Va
lue
BottleneckUtilization
Cutoff Value
CNDS 2001, Phoenix, AZ
Simulation Experiments (cont’d)
Bottleneck queue length in Experiment 1
0
10
20
30
40
50
60
0.4
5.2 10
14.8
19.6
24.4
29.2 34
38.8
43.6
Simulation Time (secs)
Bo
ttle
ne
ck Q
ue
ue
Le
ng
th
(pa
cke
ts)
Bottleneck QueueLength
CNDS 2001, Phoenix, AZ
Simulation Experiments (cont’d)
Bottleneck utilization and cutoff in Experiment 4
00.10.20.30.40.50.60.70.80.9
1
0.4
4.4
8.4
12.4
16.4
20.4
24.4
28.4
32.4
36.4
40.4
44.4
Simulation Time (secs)
Uti
liza
tio
n a
nd
Cu
toff
Va
lue
BottleneckUtilization
Cutoff Value
CNDS 2001, Phoenix, AZ
Simulation Experiments (cont’d)
Bottleneck queue length in Experiment 4
0
10
20
30
40
50
60
0.4
5.2 10
14.8
19.6
24.4
29.2 34
38.8
43.6
Simulation Time (secs)
Bo
ttle
ne
ck Q
ue
ue
Le
ng
th
(pa
cke
ts)
Bottleneck QueueLength
CNDS 2001, Phoenix, AZ
Simulation Experiments (cont’d)
Volume allocations to customers Experiment 1
00.020.040.060.080.1
0.120.140.160.180.2
0.52
4.11
7.71
11.3
14.9
18.5
22.1
25.7
29.3
32.9
36.5
40.1
Simulation Time (secs)
All
oca
ted
Vo
lum
e (
Mb
ps)
Customer 1
Customer 2
CNDS 2001, Phoenix, AZ
Simulation Experiments (cont’d)
Volume allocations to customers Experiment 4
00.010.020.030.040.050.060.070.080.090.1
0.52
4.11
7.71
11.3
14.9
18.5
22.1
25.7
29.3
32.9
36.5
40.1
Simulation Time (secs)
All
oca
ted
Vo
lum
e (
Mb
ps)
Customer 1
Customer 2
Customer 3
Customer 4
Customer 5
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 in implementing the smart market on TCP flows
• consideration of multiple diff-serv domain case
• the smart market’s behavior on bursty traffic patterns
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