10th workshop on information technologies and systems 1 a comparative evaluation of internet pricing...
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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.
Figure 6-18: Smart Market Queue Length in Experiment 2.Figure 6-17: DCC Queue Length in Experiment 2.
Figure 6-20: Smart Market Queue Length in Experiment 3.Figure 6-19: DCC Queue Length in Experiment 3.
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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
Figure 6-3: DCC Volume Allocation in Experiment 1.
Volume Allocation in Experiment 1
0
0.05
0.1
0.15
0.2
0 1 2 3 4
Time in sec
Volum
e Allo
cate
d in
Mbps
Customer 1 Customer 2
Figure 6-4: Smart Market Volume Allocation in Experiment 1.
Smart market allocates volumes more fairly than DCC
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Volume Allocation in Experiment 2
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
Customer 1 Customer 2
Volume Allocation in Experiment 2
0
0.050.1
0.15
0.20.25
0.3
0 1 2 3 4
TimeAll
ocate
d Volu
m in
Mbps
Customer 1 Customer 2
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
Volume Allocation in Experiment 3
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
Figure 6-7: DCC Volume Allocation in Experiment 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
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