Performance Study of
Congestion Price Based
Adaptive Service
Xin Wang, Henning Schulzrinne
(Columbia University)
http://www.cs.columbia.edu/xinwang/public
/projects/RNAP.html
1
Motivation
• Combine resource reservation
with multimedia adaptive
service
• Pricing network services
based on level of service,
usage, and congestion - a
natural and fair incentive for
applications to adapt their
sending rates according to
network conditions.
• Our work:
– resource commitment short
interval
– trade-offs between blocking
and raising prices in network
2
Outline
• Resource negotiation &
RNAP
• Pricing strategy
• User adaptation
• Simulation model
• Results and discussion
• RNAP message
aggregation
• Conclusion
3
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 usagesensitive) for each.
• RNAP (Resource Negotiation and
Pricing): 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
4
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
5
Protocol Architectures: Centralized
Architecture (RNAP-C)
NRN
NRN
NRN
HRN
HRN
S
1
R
1
Access Domain - A
Access Domain - B
Transit Domain
Internal Router
Edge Router
NRN Network Resource Negotiator
HRN Host Resource Negotiator
Host
Data
RNAP Messages
Intra domain messages
6
Protocol Architectures: Distributed
Architecture (RNAP-D)
HRN
HRN
S
1
R
1
Access Domain - A
Access Domain - B
Transit Domain
Internal Router
NRN
Edge Router
HRN
Host
Data
RNAP Messages
Intra domain messages
7
RNAP messages
Query: User enquires about
available services, prices
Query
Quotation
Reserve
Periodic re-negotiation
Commit
Quotation: Network specifies
services supported, prices
Reserve: User requests
service(s) for flow(s) (Flow IdService-Price triplets)
Quotation
Reserve
Commit
Close
Release
Commit: Network admits the
service request at a specific
price or denies it (Flow IdService-Status-Price)
Close: tears down negotiation
session
Release: releases the resources
8
Pricing on Current Internet
• Access rate dependent
charge (AC)
• Volume dependent
charge (V)
• AC + V
AC-V
• Usage based charging:
time-based / volumebased
9
Pricing Strategy
• Fixed pricing
– Service class independent flat
pricing
– Service class sensitive priority
pricing
– Time dependent time of day pricing
– Time-dependent service class
sensitive priority pricing
10
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 (DS)/S,0 }+, pmax]
cc(n) = pc(n) x V(n)
11
User Adaptation
• Based on perceived value
• Maximize total utility over
the total cost
• Constraint:
budget, min QoS & max
QoS
12
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.
13
Simulation Model
Topology 1
Topology 2
14
Simulation Model (cont’d.)
• Parameter Set-up
–
–
–
–
–
–
–
–
–
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
average session length 10 minutes,
exponential distributed.
• Performance measures
–
–
–
–
–
–
Bottleneck bandwidth utilization
User request blocking probability
Average and total user benefit
Network revenue
System price
User charge
15
Design of the Experiments
• Performance comparison of
congestion-based pricing system
(CPA) with a fixed-price based
system (FP)
• Effect of system control
parameters:
– target reservation rate
– price adjustment step
– price adjustment threshold
•
•
•
•
Effect of user demand elasticity
Effect of session multiplexing
Effect when part of users adapt
Session adaptation and adaptive
reservation
16
Bottleneck Utilization
Request blocking probability
17
Total network revenue ($/min)
Total user benefit ($/min)
18
Price ($/kb/min)
User bandwidth (kb/s)
19
Average user charge ($/min)
20
Effect of target utilization level
Bottleneck utilization
Request blocking probability
21
Effect of Price Adjustment Step
Bottleneck utilization
Request blocking probability
22
Effect of Price Adjustment Threshold
Bottleneck utilization
Request blocking probability
23
Effect of User Demand Elasticity
Average user bandwidth
Average user charge
24
Effect of Multiplexing Between
User Sessions
Request blocking probability
Total user benefit
25
Adaptation by Part of User Population
Bottleneck utilization
Request blocking probability
26
One-time Versus Ongoing Adaptation
Bottleneck utilization
Request blocking probability
27
RNAP Message Aggregation
RNAP-D
RNAP-C
28
RNAP Message Aggregation (cont’d)
• Aggregate for senders sharing
the same destination network
– merged by source domains
– split for HRNs at destination net:
border router (RNAP-D)
NRN (RNAP-C)
• Two messages:
– aggregated-resource message
reserves and collects price in the
middle of network
– original messages sent directly to
destination without visiting agents
in between
29
Conclusions
• CPA gain over FP
– Network availability, revenue, user
perceived benefit
– CPA congestion control is stable
and effective
• Target reservation rate
(utilization):
– too high or too low utilization:
User benefit
– Too low target rate: demand
fluctuation is high
– Too high target rate: high
blocking rate
30
Conclusions
• Effect of price scaling factor 
–  , blocking rate
–  too large
under-utilization,
large dynamics
• Effect of price adjustment
threshold 
– Too high, no meaningful
adaptation
31
Conclusions
• Demand elasticity
– Bandwidth sharing is proportional
to user’s willingness to pay
• User adaptation by some users
still results in overall
performance improvement
32