10th Workshop on Information Technologies and Systems 1 A Comparative Evaluation of Internet Pricing...

10th Workshop on Information Technologies and Systems

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A Comparative Evaluation of Internet Pricing Schemes: Smart Market and

Dynamic Capacity Contracting

T. Ravichandran Shiv Kalyanaraman

Ranjita Singh Murat Yuksel,

Rensselaer Polytechnic Institute


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Background of this research Motivations and research objectives Internet pricing models Differentiated Services Architecture Dynamic Capacity Contracting (DCC) Implementation of DCC and Smart Market Experimental results Findings Future Directions

Overview


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Background

Started with work in congestion control and traffic management

Need to integrate economic and technical measures to effectively manage congestion

Funded by a 3-year NSF grant Interdisciplinary work Proof of concept stage


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Motivations

Exponential growth in internet traffic Technical solutions such as better traffic management and

over provisioning have been used Economic measures such as responsive pricing have been

proposed but not been implemented Implementation issues have not been addressed in most

pricing models Need for better than best effort service; ability to provide

service discrimination


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Objectives

To develop internet pricing schemes taking into consideration the implementation constraints the service discrimination features of emerging internet

architectures such as the Diff-Serv transaction overhead and/or accounting costs

To evaluate the pricing schemes for both technical and economic efficiencies


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Internet Pricing Models

Static Pricing ModelsFlat rate pricingExpected capacity contracting (Clark, 1999)

Dynamic Pricing ModelsUsage-based pricingCongestion-sensitive pricing

Smart Market (MacKie- Mason & Varian, 1995)Priority-class pricing (Gupta, Stahl & Whinston, 1999)


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Differentiated-Services(Diff-Serv) Model

A standard architecture for the Internet: complex operations at network edges (i.e. edge routers) simple operations in network core (i.e. interior routers)

Expected to be the choice of ISPs and bandwidth providers Protocols for Service Level Agreement (SLA) are already available Possible to make congestion-based pricing at the edges

Ingress EdgeRouter

Egress Edge Router

Interior Router

End system

End system


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Dynamic Capacity Contracting Extends Clark’s expected capacity contracting model to incorporate

short-term contracts and adds mechanisms to make it congestion-sensitive

Customers enter into short-term contract with the service provider. Contract is specified as follows:

contract for a given traffic class is a function of volume (number of bytes), contract term (time units) and price per unit volume

Contracted volume handled with a low probability of delay and packet loss

Volume in excess of the contracted volume handled with best-effort service

Customers charged only as per contract irrespective of the actual volumes sent


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Dynamic Capacity Contracting Short-term contracts are used to provide needed flexibility to change

the price per unit volume based upon congestion Price calculation

price is set by matching demand and supply network capacity adjusted to reflect the current congestion levels price = Aggregate demand/Adjusted Capacity

P = ΣBi / [min(average_rate_limit, bottleneck_capacity)*T]where ΣBi is the total contracted amount for the previous contract term.

The average_rate_limit is updated each contract term and is based upon network congestion.


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Smart Market A congestion-sensitive pricing scheme proposed by MacKie-Mason &

Varian (1993). Imposes a per-packet-charge that reflects marginal congestion costs. A Vickery auction model for price determination Scenario:

Users assign a “bid” value for each packet and the packet tries to make through the network.

Each packet has a probability of being dropped depending on the current threshold (cutoff) bid value among the routers in the network, which depends on congestion level at the particular router, and is adjusted by that router.

Finally, users pay the highest threshold value that the packet passed through, which is the market-clearing price.


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Comparison of the ModelsSmart Market DCC Capacity

Contracting

Pricing granularity Packet Short termcontracts

Long termcontracts

Pricedetermination

Posteriori Apriori Apriori

Customerdecisions

During each periodbid price perpacket tomaximize utility

During each periodchoose volume tobe contracted at agiven price tomaximize utility

Choose a volumeto be contracted ata fixed price

Congestionsensitivity

Clearing pricedetermined byload at thebottleneck

Network capacityadjusted based oncongestion levels

Not congestionsensitive

Service assurance No serviceassurance

Contracted volumehandled with lowprobability ofdelays and packetloss

Contracted volumehandled with lowprobability ofdelays and packetloss


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Implementation of the Pricing Models We use a simple network configuration in our simulation experiments:

single bottleneck with a rate of 1Mbps customers send constant bit rate UDP traffic with fixed packet sizes (1000 bytes) contract term in DCC and the length of the feedback time interval in the smart

market are set to be 0.4sec the length of the observation interval in DCC is set to 80ms

Customers(Senders)

Bottleneck... Customers(Receivers)...

ER

ER ER

ER

IR IR


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DCC Implementation Flowchart of

actions for the customer

Start Simulation

Send request for tableof contracts and wait

Table of contractsarrived?

NO

YES

Select contract, informprovider about theselection, and wait

Has providerconfirmed the

contract?

NO

Wait for the next round.Use network serviceduring the contract

term.

YES


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DCC Implementation Flowchart of

actions for the provider (Ingress edge router)

Start Simulation

Send table of contractsand wait

Customer'sselection arrived?

NO

YES

Is capacitystill available for

contract?

Send rejection to thecustomer.

Confirm the customer

YES

Is request fortable of contracts

received?

NO

YES

Admit the traffic into thenetwork according tothe admission rules.

Updateaverage_rate_limit .

NO

Charge the customeraccording to the

advertised price and thevolume of the contract.


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DCC Implementation Updating average_rate_limit requires congestion indication from the egress

edge. How to update average_rate_limit:

Sub-divide the terms into smaller observation intervals Define rate_limit for each observation interval average_rate_limit = mean of each of the rate_limits in the observation

intervals

Ingress EdgeRouter (Shapesedge-to-edge aggregateat rate i)

Egress Edge Router (feeds back measured rate i during congestion epochs)

Interior Router(identifies congestion epochs)


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Smart Market Implementation

Smart market implementation issues packet-ordering as per bid values conflicts with TCP packet ordering assumes instant feedback of clearing price to customers which is not

feasible in a real wide area network We use deterministic time intervals at the routers (edge and interior) as a way

to handle the feedback problem. Customers get feedback from the network at the end of each time interval thereby they can make adjustments to their bid values and demands.

The length of this time interval is a comparable measure to the length of contract term in DCC. This makes the two schemes comparable


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Smart Market Implementation Flowchart of

actions of the customer

Start Simulation

Send traffic till the endof the interval by

assigning a bid to eachpacket

Is feedbackreceived?

NO

Adjust bid values,identify the number of

packets to send for thenext interval

YES

Send a "probe" packet.

Initiate bid values,length of the interval,

and the number ofpackets to send for the

first interval.


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Smart Market Implementation Flowchart of actions for the

provider (ingress edge router):

Start Simulation

Has "probe"packet received?

NO

YES

Forward the "probe"packet to the egress

edge router with highestpossible bid

Admit incoming trafficfrom the customer(s) till

the end of the interval

Has feedbackfrom egress been

received?

NO

Forward the feedbackto the customer

YES


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Smart Market Implementation Flowchart of actions for the

provider (egress edge router):

Start Simulation

Has "probe"packet received?

NO

YES

Send feedback to theingress by copying

market-clearing priceinto it

Forward the trafficcoming from thenetwork to the

destination customer(s)


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Comparative Evaluation Technical Efficiency

Bottleneck utilization Throughput and goodput Queue length

Economic efficiency Fairness in volume allocation

ExperimentNumber #1 #2 #3

1 60 60 -2 25 60 -3 15 25 35

Budgets of Customers

Parameters of the experiments


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Results Total volume allocated to all customers is significantly higher in the case of

DCC. This indicates that DCC better utilizes the bottleneck.

Experiment Total TotalNumber #1 #2 #3 Volume #1 #2 #3 Volume

1 0.18 0.19 - 0.36 0.14 0.14 - 0.282 0.14 0.25 - 0.39 0.07 0.18 - 0.253 0.09 0.12 0.15 0.36 0.04 0.07 0.11 0.22

Customer CustomerDCC Smart Market

Mean and total volumes (Mbps) allocated to customers


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Figure 6-11: DCC Bottleneck Utilization in Experiment 2. Figure 6-12: Smart Market Bottleneck Utilization in Experiment 2.

Figure 6-13: DCC Bottleneck Utilization in Experiment 3. Figure 6-14: Smart Market Bottleneck Utilization in Experiment 3.


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Results DCC achieves higher throughput and goodput with a lower packet

drop.

Performance metrics of the experiments

Experiment Goodput Throughput Packets Goodput Throughput PacketsNumber (Mbps) (Mbps) Dropped (Mbps) (Mbps) Dropped

1 0.964 0.966 33 0.700 0.748 422 0.958 0.958 40 0.615 0.663 433 0.944 0.946 35 0.537 0.583 40

DCC Smart Market


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Results Both models seem to do well in clearing queues at the bottleneck

Figure 6-16: Smart Market Queue Length in Experiment 1.Figure 6-15: DCC Queue Length in Experiment 1.




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Results

Figure 6-7: DCC Volume Allocation in Experiment 3.

Volume Allocation in Experiment 1

0

0.05

0.1

0.15

0.2

0.25

0.3

0 1 2 3 4

Time in sec

Volu

me

allo

cate

d in

M

bps

Customer 1 Customer 2



0

0.05

0.1

0.15

0.2

0 1 2 3 4

Time in sec

Volum

e Allo

cate

d in

Mbps


Figure 6-4: Smart Market Volume Allocation in Experiment 1.

Smart market allocates volumes more fairly than DCC


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0

0.1

0.2

0.3

0.4

0 1 2 3 4

Time in sec

Volu

me

allo

cate

d in

M

bps



0

0.050.1

0.15

0.20.25

0.3

0 1 2 3 4

TimeAll

ocate

d Volu

m in

Mbps


Figure 6-5: DCC Volume Allocation in Experiment 2. Figure 6-6: Smart Market Volume Allocation in Experiment 2.

Results


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Results


0

0.05

0.1

0.15

0.2

0.25

0 1 2 3 4

Time in sec

Volu

me

alloc

ated

in

Mbp

s

Customer 1 Customer 2 Customer 3


Volume Allocationin Experiment 3

0

0.05

0.1

0.15

0 1 2 3 4

Time in sec

Volum

e allo

cated

in

Mbps

Customer 1 Customer 2 Customer 3

Figure 6-8: Smart Market Volume Allocation in Experiment 3.


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Summary


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Future Directions


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Questions and Comments

10th Workshop on Information Technologies and Systems 1 A Comparative Evaluation of Internet Pricing...

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