Performance Study of Congestion Price Based Adaptive Service

1
Performance Study of Congestion Price Based
Adaptive Service

• Xin Wang, Henning Schulzrinne
• (Columbia University)
• http//www.cs.columbia.edu/xinwang/public/projects
  /RNAP.html

2
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

3
Outline

• Resource negotiation RNAP
• Pricing strategy
• User adaptation
• Simulation model
• Results and discussion
• RNAP message aggregation
• Conclusion

4
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 (Resource Negotiation and Pricing) a
  protocol through which the user and network (or
  two network domains) negotiate network delivery
  services.
• Network -gt User communicate availability of
  services price quotations and accumulated
  charges
• User -gt Network request/re-negotiate specific
  services for user flows.
• Underlying Mechanism combine network pricing
  with traffic engineering

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Resource Negotiation RNAP (contd.)

• 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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Protocol Architectures Centralized Architecture
(RNAP-C)
NRN
NRN
NRN
HRN
HRN
S1
R1
Access Domain - A
Access Domain - B
Transit Domain
Internal Router
NRN
Network Resource Negotiator
Host Resource Negotiator
Edge Router
HRN
Data
Host
Intra domain messages
RNAP Messages
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Protocol Architectures Distributed Architecture
(RNAP-D)
HRN
HRN
S1
R1
Access Domain - A
Access Domain - B
Transit Domain
Internal Router
NRN
Edge Router
HRN
Data
Host
Intra domain messages
RNAP Messages
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RNAP messages
Query User enquires about available services,
prices
Query
Quotation
Quotation Network specifies services supported,
prices
Reserve
Reserve User requests service(s) for flow(s)
(Flow Id-Service-Price triplets)
Commit
Quotation
Commit Network admits the service request at a
specific price or denies it (Flow
Id-Service-Status-Price)
Reserve
Periodic re-negotiation
Commit
Close tears down negotiation session
Close
Release releases the resources
Release
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Pricing on Current Internet

• Access rate dependent charge (AC)
• Volume dependent charge (V)
• AC V AC-V
• Usage based charging time-based / volume-based

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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

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Pricing Strategy (contd.)

• 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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User Adaptation

• Based on perceived value
• Maximize total utility over the total cost
• Constraint
  budget, min QoS max QoS

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User Adaptation (contd.)

• 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 / Sl ? l )
  / pj
• Affordable resource is distributed proportionally
  among applications of the system, based on the
  users preference and budget for each application.

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Simulation Model
Topology 1
Topology 2
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Simulation Model (contd.)

• 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 s 0.06
• price update threshold ? 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

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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

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Bottleneck Utilization
Request blocking probability
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Total network revenue (/min)
Total user benefit (/min)
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Price (/kb/min)
User bandwidth (kb/s)
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Average user charge (/min)
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Effect of target utilization level
Bottleneck utilization
Request blocking probability
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Effect of Price Adjustment Step
Bottleneck utilization
Request blocking probability
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Effect of Price Adjustment Threshold
Bottleneck utilization
Request blocking probability
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Effect of User Demand Elasticity
Average user bandwidth
Average user charge
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Effect of Multiplexing Between User Sessions
Request blocking probability
Total user benefit
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Adaptation by Part of User Population
Bottleneck utilization
Request blocking probability
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One-time Versus Ongoing Adaptation
Bottleneck utilization
Request blocking probability
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RNAP Message Aggregation
RNAP-D
RNAP-C
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RNAP Message Aggregation (contd)

• 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

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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

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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

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Conclusions

• Demand elasticity
• Bandwidth sharing is proportional to users
  willingness to pay
• User adaptation by some users still results in
  overall performance improvement