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 -> 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. Centralized Architecture (RNAP-C)
NRN
NRN
NRN
HRN
HRN S1 R1
Access Domain - A
Access Domain - B
We consider two alternative architectures for implementing RNAP in the network, a centralized architecture (RNAP-C) and a distributed architecture (RNAP-D)
In RNAP-C, user negotiates through a HRN, each network domain has a NRN.
When a user wants to to apply for resources from the network, it first sends a request to the NRN of its access domain.
This request is then propagated to next-domain NRN, and so on.
In general, each NRN is in charge of admission control, price quotation and charging for its domain.
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
We consider two alternative architectures for implementing RNAP in the network, a centralized architecture (RNAP-C) and a distributed architecture (RNAP-D)
In RNAP-C, user negotiates through a HRN, each network domain has a NRN.
When a user wants to to apply for resources from the network, it first sends a request to the NRN of its access domain.
This request is then propagated to next-domain NRN, and so on.
In general, each NRN is in charge of admission control, price quotation and charging for its domain.
Transit Domain
Internal Router
HRN
Host Resource Negotiator
Edge Router
RNAP Messages
Host
Data

7. Resource Negotiation & RNAP, Cont’d
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: Fixed pricing
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
AC: unlimited use
limited duration + per hour charge
AC-V: allow for some amount of volume to be free, then per-volume charge
Time-based: more fit for telephone
Wide range of applications need volume based charging

9. 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 =α i x (pui - pu i-1) ch (n) = ph x R(n) x τ
Congestion charge: pc (n) = min [{pc (n-1) + σ (D, S) x (D-S)/S,0 }+, pmax] cc(n) = pc(n) x V(n)

10. Pricing Strategy, Cont’d
A generic pricing structure
Cost = cac(rac) + p (rac) x (t-tm) ΣiΣn [phi(n) x ri(n) xτ + (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;
τ: negotiation period
tm: the minimum time without charge
vm: the volume transferred free of charge

11. Based on perceived value Application adaptation
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, Cont’d
An example utility function
U (x) = U0 + ω log (x / xm)
Optimal user demand
Without budget constraint: xj = ωj / pj
With budget constraint: xj = (b x ωj / Σl ωl ) / pj
Affordable resource is distributed proportionally among applications of the system, based on the user’s preference and budget for each application.

14. Simulation Model

15. Simulation Model

16. Simulation Model, Cont’d
Parameters Set-up
topology1: 48 users
topology 2: 360 users
user requests: 60 kb/s 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

17. Simulation Model, Cont’d
Performance measures
Bottleneck bandwidth utilization
User request blocking probability
Average and total user benefit
Network revenue
System price
User charge
- Offered load: total user resource requirement at the bottleneck over the bottleneck capacity
- Bottleneck bandwidth utilization: averaging the reserved bandwidth (ratio of the link capacity) over all negotiation periods
- User request blocking probability: percentage of user requests being denied
- Average and total user benefit: utility. Not benefit for a blocked call.
- Network revenue: total charge paid to the network for all the admitted requests during a simulation
-System price: Different along different paths.
-User charge: based on bandwidth and price quoted.

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
- Offered load: total user resource requirement at the bottleneck over the bottleneck capacity
- Bottleneck bandwidth utilization: averaging the reserved bandwidth (ratio of the link capacity) over all negotiation periods
- User request blocking probability: percentage of user requests being denied
- Average and total user benefit: utility. Not benefit for a blocked call.
- Network revenue: total charge paid to the network for all the admitted requests during a simulation
-System price: Different along different paths.
-User charge: based on bandwidth and price quoted.

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 Target reservation rate (utilization):
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 σ
σ , blocking rate
Too large σ, under-utilization, large dynamics
Effect of price adjustment threshold θ
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