Performance Study of
Congestion Price Based
Adaptive Service
Xin Wang,
Henning Schulzrinne
(Columbia university)
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Outline
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Resource negotiation & RNAP
Pricing strategy
User adaptation
Simulation model
Results and discussion
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Resource Negotiation & RNAP
• Assumption: network provides a choice of delivery
services to user
– e.g. diff-serv, int-serv, best-effort, with different levels of QoS
– with a pricing structure (may be usage-sensitive) for each.
• RNAP: a protocol through which the user and
network (or two network domains) negotiate network
delivery services.
– Network -> User: communicate availability of services; price
quotations and accumulated charges
– User -> Network: request/re-negotiate specific services for
user flows.
• Underlying Mechanism: combine network pricing with
traffic engineering
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Resource Negotiation & RNAP (cont’d.)
• Who can use RNAP?
– Adaptive applications: adapt sending rate,
choice of network services
– Non-adaptive applications: take fixed
price, or absorb price change
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Centralized Architecture (RNAP-C)
NRN
HRN
NRN
NRN
HRN
S1
R1
Access Domain - A
Access Domain - B
Transit Domain
Internal Router
NRN Network Resource Negotiator
Edge Router
HRN Host Resource Negotiator
Host
RNAP Messages
Data
Intra domain messages
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Distributed Architecture
(RNAP-D)
HRN
HRN
S1
R1
Access Domain - A
Access Domain - B
Transit Domain
Internal Router
HRN Host Resource Negotiator
Edge Router
RNAP Messages
Host
Data
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Resource Negotiation & RNAP (cont’d.)
Query
Quotation
Periodic re-negotiation
Reserve
Commit
Quotation
Reserve
Commit
Query: User enquires about available
services, prices
Quotation: Network specifies services
supported, prices
Reserve: User requests service(s) for
flow(s) (Flow Id-Service-Price triplets)
Commit: Network admits the service
request at a specific price or denies it
(Flow Id-Service-Status-Price)
Close: tears down negotiation session
Release: release the resources
Close
Release
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Pricing Strategy
• Current Internet:
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Access rate dependent charge (AC)
Volume dependent charge (V)
AC + V
AC-V
Usage based charging: time-based,
volume-based
• Fixed pricing
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Service class independent flat pricing
Service class sensitive priority pricing
Time dependent time of day pricing
Time-dependent service class
sensitive priority pricing
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Pricing Strategy (cont’d.)
• Congestion-based Pricing
– Usage charge:
pu= f (service, demand, destination, time_of_day, ...)
cu(n) = pu x V (n)
– Holding charge:
Phi =  i x (pui - pu i-1)
ch (n) = ph x R(n) x 
– Congestion charge:
pc (n) = min [{pc (n-1) +  (D, S) x (D-S)/S,0 }+, pmax]
cc(n) = pc(n) x V(n)
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Pricing Strategy (cont’d.)
• A generic pricing structure
– Cost = cac(rac) + p (rac) x (t-tm)+ +
in [phi(n) x ri(n) x + (pui(n) + pci (n)) x vi (n)]
(vi-vmi)+
• cac: access charge; rac : access rate
• p (rac): unit time price
• i: class i; n: nth negotiation interval;
•  : negotiation period
the minimum time without charge
• vm: the volume transferred free of charge
t m:
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User Adaptation
• Based on perceived value
• Application adaptation
– Maximize total utility over the total cost
– Constraint:
budget, min QoS & max QoS
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CPA & FP
• CPA: congestion price based
adaptive service
• FP: fixed price based service
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User Adaptation (cont’d.)
• An example utility function
– U (x) = U0 +  log (x / xm)
• Optimal user demand
– Without budget constraint: xj = j / pj
– With budget constraint: xj = (b x  j / Σl  l ) / pj
• Affordable resource is distributed proportionally among
applications of the system, based on the user’s
preference and budget for each application.
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Simulation Model
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Simulation Model
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Simulation Model (cont’d.)
• Parameters Set-up
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topology1: 48 users
topology 2: 360 users
user requests: 60 kb/s -- 160 kb/s
targeted reservation rate: 90%
price adjustment factor: σ = 0.06
price update threshold: θ = 0.05
negotiation period: 30 seconds
usage price: pu = 0.23 cents/kb/min
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Simulation Model (cont’d.)
• Performance measures
– Bottleneck bandwidth utilization
– User request blocking probability
– Average and total user benefit
– Network revenue
– System price
– User charge
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Design of the Experiments
• Performance comparison of CPA & FP
• Effect of system control parameters:
– target reservation rate
– price adjustment step
– price adjustment threshold
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Effect of user demand elasticity
Effect of session multiplexing
Effect when part of users adapt
Session adaptation and adaptive reservation
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Performance Comparison of CPA and FP
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Bottleneck Utilization
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Request blocking probability
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Total network revenue ($/min)
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Total user benefit ($/min)
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Average user benefit ($/min)
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Price ($/kb/min)
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User bandwidth (kb/s)
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Average price ($/kb/min)
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Average user bandwidth (kb/s)
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Average user charge ($/min)
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Effect of target reservation rate
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Bottleneck utilization
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Request blocking probability
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Total user benefit
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Effect of Price Adjustment Step
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Bottleneck utilization
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Request blocking probability
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Effect of Price Adjustment Threshold
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Request blocking probability
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Effect of User Demand Elasticity
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Average user bandwidth
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Average user charge
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Effect of Session Multiplexing
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Request blocking probability
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Total user benefit
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Effect When Part of Users Adapt
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Bandwidth utilization
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Request blocking probability
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Session Adaptation & Adaptive Reservation
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Bandwidth utilization
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Blocking probability
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Conclusions
• CPA gain over FP
– Network availability, revenue, perceived benefit
– Congestion price as control is stable and
effective
• Target reservation rate (utilization):
– User benefit , with too high or too low
utilization
– Too low target rate, demand fluctuation is high
– Too high target rate, high blocking rate
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Conclusions
• Effect of price scaling factor 
–  , blocking rate
– Too large , under-utilization, large dynamics
• Effect of price adjustment threshold 
– Too high, no meaningful adaptation
– Too low, no big advantage
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Conclusions
• Demand elasticity
– Bandwidth sharing is proportional to its
willingness to pay
• Portion of user adaptation results in overall
system performance improvement
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