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 -> 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 at application-appropriate intervals  TCP intervals may not be useful for audio/video • Non-adaptive applications: take fixed (risk-adjusted) price, or absorb price change • Can be own protocol or objects in RSVP

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 (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: • Access rate dependent charge (AC) • Volume dependent charge (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

9. Pricing Strategy (cont’d.) • Congestion-based Pricing • Usage charge: pu= f (service, demand, destination, time of day, ...) cu(n) = pu V (n) • Holding charge: Phi = i(pui - pui-1)ch (n) = ph R(n)  • Congestioncharge: pc (n) = min [{pc (n-1) + (D, S) (D-S)/S,0 }+, pmax] cc(n) = pc(n) V(n)

10. Pricing Strategy (cont’d.) • A generic pricing structure • Cost = cac(rac) + p (rac) x (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: negotiation interval; •  : negotiation period tm: the minimum time without charge • vm: the volume transferred free of charge

11. User Adaptation • Based on perceived value (not generic QoS/SNR measure) • 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 + log (x / xm) • Optimal user demand • Without budget constraint: xj= j / pj • With budget constraint: xj= (b x j / Σll) / pj • Affordableresource 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 -- 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

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

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