Performance Study of Congestion Price Based Adaptive Service

1
Performance Study of Congestion Price Based
Adaptive Service

• Xin Wang, Henning Schulzrinne
• (Columbia university)

2
Outline

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

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 usage-sensitive)
  for each.
• RNAP 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

4
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

5
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
Edge Router
Host Resource Negotiator
Data
HRN
Host
Intra domain messages
RNAP Messages
6
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
7
Resource Negotiation RNAP, Contd
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)
Periodic re-negotiation
Reserve
Commit
Close tears down negotiation session
Close
Release release the resources
Release
8
Pricing Strategy

• Current Internet
• Access rate dependent charge (AC)
• Volume dependent charge (V)
• AC V AC-V
• Usage based charging time-based, volume-based
• 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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Pricing Strategy, Contd

• A generic pricing structure
• Cost cac(rac) p (rac) (t-tm) ?i ? n
  phi(n) ri(n) ? (pui(n) pci (n)) vi
  (n) (vi-vmi)
• cac access charge rac access rate
• p (rac) unit time price
• i class i n nth negotiation interval
• ? negotiation period
• tm the minimum time without charge
• vm the volume transferred free of charge

11
User Adaptation

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

12
CPA FP

• CPA congestion price based adaptive service
• FP fixed price based service

13
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.

14
Simulation Model
15
Simulation Model
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Simulation Model, Contd

• Parameters 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

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Simulation Model, Contd

• Performance measures
• Bottleneck bandwidth utilization
• User request blocking probability
• Average and total user benefit
• Network revenue
• System price
• User charge

18
Design of the Experiments

• Performance comparison of CPA 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

19
Performance Comparison of CPA and FP
20
Bottleneck Utilization
21
Request blocking probability
22
Total network revenue (/min)
23
Total user benefit (/min)
24
Average user benefit (/min)
25
Price (/kb/min)
26
User bandwidth (kb/s)
27
Average price (/kb/min)
28
Average user bandwidth (kb/s)
29
Average user charge (/min)
30
Effect of target reservation rate
31
Bottleneck utilization
32
Request blocking probability
33
Total user benefit
34
Effect of Price Adjustment Step
35
Bottleneck utilization
36
Request blocking probability
37
Effect of Price Adjustment Threshold
38
Request blocking probability
39
Effect of User Demand Elasticity
40
Average user bandwidth
41
Average user charge
42
Effect of Session Multiplexing
43
Request blocking probability
44
Total user benefit
45
Effect When Part of Users Adapt
46
Bandwidth utilization
47
Request blocking probability
48
Session Adaptation Adaptive Reservation
49
Bandwidth utilization
50
Blocking probability
51
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

52
Conclusions

• Effect of price scaling factor s
• s , blocking rate
• Too large s, under-utilization, large dynamics
• Effect of price adjustment threshold ?
• Too high, no meaningful adaptation
• Too low, no big advantage

53
Conclusions

• Demand elasticity
• Bandwidth sharing is proportional to its
  willingness to pay
• Portion of user adaptation results in overall
  system performance improvement