A Strategy for Implementing
Smart Market Pricing Scheme
on Diff-Serv
Murat Yuksel and Shivkumar Kalyanaraman
Rensselaer Polytechnic Institute, Troy, NY
[email protected], [email protected]
Outline
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Literature development :
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congestion-sensitive pricing
the Smart Market (SM) pricing scheme
Adaptation of SM to diff-serv
Simulation experiments
Summary
Congestion-Sensitive Pricing
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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:
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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)
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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)
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The price for each packet is the highest
cutoff value it passed through, i.e.
market-clearing price.
Why is SM important?
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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
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For data plane packets:
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Edge routers (ERs):
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Interior Routers (IRs):
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write the bid value (b) to the packet header
and then send the packet into the core
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:
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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)
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ERs and customers:
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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 clearingprice 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:
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update the threshold (T) value at every τ
update control packets’ clearing-price field too
Cutoff Value, T
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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
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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:
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SM-SORTED: higher bids have priority at IRs
SM-FIFO: first-come first served
Simulation Experiments (cont’d)
Single-Bottleneck
0
flow 0
flow 1
15Mb
1
15Mb
15Mb
2
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0
A
10Mb
flow 2
15Mb
B
15Mb
1
15Mb
2
3 user flows with budgets 100, 75 and 25 $/Mb.
Total simulation time is 3000s.
Simulation Experiments (cont’d)
Simulation Experiments (cont’d)
Simulation Experiments (cont’d)
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To observe service differentiation:
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Two flows with a varying ratio of budgets.
Simulation Experiments (cont’d)
1
2
3
15Mb
15Mb
15Mb
Multi-Bottleneck
flow 0
0
15Mb
A
10Mb
B
flow 1
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10Mb
C
flow 2
10Mb
D
flow 3
15Mb
15Mb
15Mb
1
2
3
Each user flow has a budget of 10$/Mb.
15Mb
0
Simulation Experiments (cont’d)
Summary
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Major changes to SM are need for an
implementation on diff-serv
By extensive simulation we observed that:
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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