Need of Time-of-day Internet Access Management

1. Need of Time-of-day Internet Access Management • Peak-hour bandwidth utilization  100%(9 a.m.–3 a.m.) • Peak-hour drop rate > 3 Mbps • Peak-hour usage: Heavy : normal = 13.08: 1 Q: What problems do we suffer over free-of-charge or flat rate network? - Ex: NTU dorm networks How to 1) Manage the time-of-day Internet access 2) Design an incentive control scheme

2. Research on Time-of-day Internet Access Management by Quota-based Priority Control Presented by Shao-I Chu Advisor: Dr. Shi-Chung Chang Date: June 13, 2007

3. Outline • Existing Quota-based Priority Control • User Behavior: Prudent and Myopic • Design of Management scheme I: Game theoretic virtual pricing • Design of Management scheme II: Heuristic-based Quota Scheduling • Performance Comparisons • Conclusions

4. Outline • Existing Quota-based Priority Control • User Behavior: Prudent and Myopic • Design of Management scheme I: Game theoretic virtual pricing • Design of Management scheme II: Heuristic-based Quota Scheduling • Performance Comparisons • Conclusions

5. Quota-based Priority Control (QPC) • Solve abusiveand unfair usage • Missions of network manager • Meet majority users’ basic demand • Limit heavy users’ abusive usage • QPC Services • Regularservice (high priority) – daily quota limitation • Custody service (low priority)

6. Existing QPC Architecture • Merits of QPC • Daily congestion improved by 48% • Over 91% users’ usage encouraged • Weakness of QPC • - No consideration on temporal effect 54Mbps

7. Outline • Existing Quota-based Priority Control • User Behavior: Prudent and Myopic • Design of Management scheme I: Game theoretic virtual pricing • Design of Management scheme II: Heuristic-based Quota Scheduling • Performance Comparisons • Conclusions

8. Myopic and Prudent Behaviors under QPC • MyopicUser: no consideration on quota limitation (6:00 am quota renewal) • Prudent User: careful allocation of one’s quota

9. How to Design the Management Schemes M1) How to effectively manage the time-of-day Internet access by utilizing minimal empirical data M2) How to design a simple and incentive control scheme for easy acceptance by users M3) How to combine the existing QPC architecture M4) How to construct a design methodology for a changing network

10. Design of Management Schemes • Virtual Pricing • price=number of quota per byte • Price varies with time • Quota Scheduling • Different quota allocations for different time periods Ex: Time length: peak: off-peak=1:1 Incentive!!

11. Contributions of This Thesis • Propose virtual pricing and compare it with quota scheduling and for time-of-day Internet access management • effective by utilizing minimal empirical data to model user behaviors • incentive and flexible • easily combined with QPC • generic design methodology constructed for a changing network

12. Outline • Existing Quota-based Priority Control • User Behavior: Prudent and Myopic • Design of Management scheme I: Game theoretic virtual pricing • Design of Management scheme II: Heuristic-based Quota Scheduling • Performance Comparisons • Conclusions

13. Challenges for Virtual Pricing Design P1) How to exploit empirical to model user response w.r.t. price P2) How to design a pricing policy to maximize bandwidth utilization P3) How to design a simple pricing policy for user acceptance P4) How to exploit the existing hardware and software of the legacy network P5) How to design a methodology To answer P3) and P4)  Static Time-of-day Pricing (TDP)

14. Design Methodology of TDP Manager’s Decision: New Policy Needed? Network Performance Monitoring To answer P5): How to design a methodology

15. Step 1: Pilot Experiment and Analysis Baseline Experiment Analysis QPC Experiment Analysis Design Methodology of TDP Step 1): • Baseline Experiment - No quota limitation - Characterize network problem and user original demand • QPC Experiment • - Daily quota • - Provide data for constructing user models

16. Design Methodology of TDP Step 2): • Construct myopicand prudent User behavior - Varies with price profile and demand - User preference estimated by QPC experiment Step 2: Empirical User Demand Model Construction

17. Design Methodology of TDP Step 3): • Leader: network manager • - Maximize the total bandwidth utilization • - keep the total demand below the capacity • Followers: users - Maximize their own benefits Step 3: Time-of-day Pricing Design Using Game-theoretic Problem Formulation

18. Design Methodology of TDP Step 4): • Perform numerical assessment based on empirical data under pricing policy by step 3 • Exploit the experimental data of step 1 and user demand model constructed by step 2 to simulate user behavior. Step 4: Network Performance and User Prediction by Simulation

19. Design Methodology of TDP Manager’s Review And Adjustment

20. 1 (prudent) 0 (myopic) price profile & daily demand (baseline experiment) Daily demand A=Q/max{pk} B=Q/min{pk} Myopic and Prudent User Classification To answer P1): How to exploit empirical data to model user response w.r.t. price Prudent User Myopic User

21. F(.) Satisfaction volume Myopic User Model • Focus on short-term benefit maximization • Maximize i’s own benefit at that time slot k only User preference diminishing returns of scale

22. Prudent User Model • Focus on daily benefit maximization • Maximize i’s total benefit from time slot k to time slot K subject to the quota budget constraint

23. How to Estimate Individual User Preference • Derive preference from optimal conditions User Usage Data under QPC (pk=1)

24. Selection of F(.) • Myopic user: • Prudent user: • User preference: Utility(Rate)=log(Rate)  Utility(Volume)=log(Volume) i.e., F(. )=log(. )

25. User Behavior Model w.r.t. Price Myopic Prudent User Preferences User Volume under Baseline Experiment {pk|k=1,2,…,K} User Classification To answer P1): How to exploit empirical data to model user response w.r.t. price User Volume under QPC Experiment Utility Function F(.)=log(. )

26. TDP Design • Manager’s Decision Problem: To answer P2): How to design a pricing design to maximize total bandwidth utilization Price Profile Goal of Network Manager When service is free or flat rate Maximize total bandwidth utilization of regular service Total User submission cannot exceed the bandwidth

27. Volume Volume Leader-follower Model Leader-Network Manager Goal: Maximize total bandwidth utilization Price Follower-Users Prudent User Maximize daily benefits Myopic User Maximize short-term benefits

28. Analysis of TDP Policy • Goals • How the prices may induce user behavior and affect network performance • How TDP policy varies w.r.t user behavior • Problem Settings • - 3 users • - 3 time units • bandwidth:10 units • price set

29. Why Needs User Differentiation • Case I • Pricing policy :prudent, Users: myopic • Case II • Pricing policy : myopic, Users: prudent Submitted volumes are not shaved (>10) - Bandwidth utilization < 50% at time slots 1 and 2 - Congestion happens at time slot 3

30. Congestion! Pricing Policy for Prudent Users • Hypotheses: • The higher user preference  the higher price for a time slot • Analyses: • Due to link capacity constraint • Q=10  P=(1,2,3) • Q=25  P=(2,3,4)

31. Congestion! Not shaved! Pricing Policy for Myopic Users • The property that no longer holds • Analysis: • Due to link capacity constraint • Q=10  P=(2,3,1) • Q=20  P=(2,3,3)

32. Effectiveness Evaluation • Parameter Setting • Peak hour  9 a.m. to 3 a.m • Quota replenishment point  6:00 a.m. • Length of each time slot 10 minutes. • Bottleneck bandwidth  54Mbps. • Admissible price set (per byte): Ω={1, 1.1, 1.2, 1.3, 1.4, 1.5} • Quota budget of each user  1G • Hypotheses • Optimal price: • Drop rate of regular service  0 • Peak-hour usage: • - Total submitted volume of regular service ↓ • - User transmitted volume of Internet access ↓

33. Peak Shaving and Load Balancing Effects • Optimal Price  Total submitted rate reduced by 11.53% during peak hours Difference between peak and off-peak hours reduced by 31.21% TDP effectively manages the time-of-day Internet access ! Peak-hour drop rate reduced to 0

34. Peak-hour Abuse Improvement • Abuse Index – Top 5 user Internet usage

35. Peak-hour Fairness Improvement • Fairness Index– Standard deviation of Internet usage TDP improves peak-hour abuse and unfairness

36. Policy Adaptation to Changes Short time period for data collection: • Baseline and QPC experiments will be conducted for a short period (1 week each) • Only conducted at the beginning of a new academic year • Fast policy design and evaluation • Takes several minutes in the case of the NTU dormitory network with 5000+ users

37. Outline • Existing Quota-based Priority Control • User Behavior: Prudent and Myopic • Design of Management scheme I: Game theoretic virtual pricing • Design of Management scheme II: Heuristic-based Quota Scheduling • Performance Comparisons • Conclusions

38. Load Balancing-based Quota Scheduling (LB-QS) • Objective: • Equalize the average traffic of peak and off peak hours • Designed Quota Scheduling:

39. Total Submission Rate under QPC d Bandwidth Limitation d Peak Hours Off-peak Hours Peak Shaving-based Quota Scheduling (PS-QS) User Quota Usage  Total Submission Scheduled Quota Estimated User Quota Usage

40. Outline • Existing Quota-based Priority Control • User Behavior: Prudent and Myopic • Design of Management scheme I: Game theoretic virtual pricing • Design of Management scheme II: Heuristic-based Quota Scheduling • Performance Comparisons • Conclusions

41. Comparisons of TDP and QS: Load Balancing & Peak Shaving • Peak Shaving Index (PSI): • Average total submission rate of peak hours • Load Balancing Index (LBI) • Difference of average total submission rates between peak and off-peak hours • Evaluated over the empirical data of NTU dormitory • network • LB-QS (Qpeak,Qoff-peak)=(750MB, 250MB) • No user usage data needed • PS-QS (Qpeak,Qoff-peak)=(620MB, 380MB) • User usage data of QPC • TDP (Ppeak,Poff-peak)=(1.3, 1.1) • User usage data of QPC • Baseline data (no control) • LBI and PSI under PS-QS improved by 37.6% and 4.7% over LB-QS because of considerations on user preferences over time

42. Comparisons of TDP and QS: Total Submission Rate • a spike (congestion) at 9 a.m. because of no price and no user differentiation • PS-QS encourages more usage than LB-QS because of user preference

43. Comparisons of TDP and QS: Abuse and Fairness Improvement • Abuse Index (AI) • Internet access volume by top 5 users • Fairness Index (FI) • Standard deviation among all users’ usage • TDP outperforms QS by at least 14% • PS-QS is better than LB-QS (user preferences )

44. Design Related Issues

45. Outline • Existing Quota-based Priority Control • User Behavior: Prudent and Myopic • Design of Management scheme I: Game theoretic virtual pricing • Design of Management scheme II: Heuristic-based Quota Scheduling • Performance Comparisons • Conclusions

46. Conclusions (1/2) • Propose a incentive and simple control scheme TDP over free-of-charge or flat rate network (M2,P3) • TDP is easily implemented over QPC (M3, P4) • TDP develop empirical data-based user model (P1) • Myopic and prudent users • TDP uses game-theoretic design to maximize bandwidth utilization (P2) • Network manager as leader, users as followers

47. Conclusions (2/2) • TDP effectively manages the time-of-day Internet access traffic (M1) • Peak-hour abuse and fairness improved by 14% above over QS • Load balancing and peak shaving reduced by 24% and 9% • Generic methodology of TDP is proposed for a changing network (M4, P5) • Two short-period data collections • Fast evaluation and design in several minutes • Apply to campus, government, community and corporate LANs

48. Thanks for your attention!