Simulating the Smart Market Pricing Scheme on DifferentiatedServices Architecture

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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_at_cs.rpi.edu, shivkuma_at_ecse.rpi.edu

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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

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Three Basic Pricing Strategies

• Flat-rate pricing
• fixed price for a time period, F where F0
• Usage-based pricing
• plus fixed price for unit amount of traffic, FT
  where F0 and T0
• Congestion-sensitive pricing
• plus varying price based upon congestion level of
  the network, FTC where F0, T0 and C0

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Congestion-Sensitive Pricing

• A way of controlling users 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 dont 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.

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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.

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The Smart Market (contd)

• 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?

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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

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Deployment on Diff-Serv? (contd)

• 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.

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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
• if bT, update the packets clearing-price field
  and forward it
• For control plane packets
• ERs and IRs maintain a time interval (t) 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 t.

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Adaptation to Diff-Serv (contd)

• ERs and customers
• Ingress ER sends a probe packet to the network
  core at every t 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 t
• update control packets clearing-price field too

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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?

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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

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Customer Model (contd)

• 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 / 4p2 ? number of packets to send in
  the next interval!
• If customers budget is not enough for that value
  of x, then she/he lowers it to x Budget / p.

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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, t.
• IRs maps T values to 0,1, and hence loose
  accuracy

Steady state cutoff value, T, for different
customer budgets
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Simulation Experiments
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 t is 0.4s 400ms.

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Simulation Experiments (contd)
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

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Simulation Experiments (contd)
Bottleneck utilization and cutoff in Experiment 1.
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Simulation Experiments (contd)
Bottleneck queue length in Experiment 1
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Simulation Experiments (contd)
Bottleneck utilization and cutoff in Experiment 4
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Simulation Experiments (contd)
Bottleneck queue length in Experiment 4
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Simulation Experiments (contd)
Volume allocations to customers Experiment 1
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Simulation Experiments (contd)
Volume allocations to customers Experiment 4
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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.

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Future Work

• a thorough investigation of difficulties in
  implementing the smart market on TCP flows
• consideration of multiple diff-serv domain case
• the smart markets behavior on bursty traffic
  patterns