1 need of time-of-day internet access management peak-hour bandwidth utilization 100%(9 a.m.–3...
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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
User Group
Average Usage (Bytes)
2GHU 17,004,173
1GHU 24,530,722
100MHU 43,410,386
NU 4,754,360
LU 1,736,643
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 access2) Design an incentive control scheme
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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
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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 abusive and unfair usage• Missions of network manager
– Meet majority users’ basic demand – Limit heavy users’ abusive usage
• QPC Services– Regular service (high priority) – daily
quota limitation– Custody service (low priority)
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Existing QPC Architecture
Dormitory Network
Dormitory Network
Router(Cabletron SSR2000)
TANet
Router(packet engine 5022)
Metering Router(Cisco 7513)
NTU Domain
54Mbps
QoS Router
DB
Accounting and Traffic Control Server
Web-based Service Management Server
Meter Reading Server
54Mbps
Merits of QPC - Daily congestion improved by 48%- Over 91% users’ usage encouragedWeakness of QPC- No consideration on temporal effect
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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
• Myopic User: no consideration on quota limitation (6:00 am quota renewal)
• Prudent User: careful allocation of one’s quota
0 2 4 6 8 10 12 14 16 18 20 22 240
1
2
3
4
5
6
7
8x 10
7
Day Time
Inte
rnet
Usage (
Byte
s)
0 2 4 6 8 10 12 14 16 18 20 22 240
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5x 10
7
Day Time
Inte
rnet
Usage (
Byte
s)
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
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Ex: Time length: peak: off-peak=1:1
Design of Management Schemes
• Quota Scheduling– Different quota allocations for different time
periods
Peak Hours
Off-peak Hours
User B
• Virtual Pricing – price=number of quota per byte– Price varies with time
Peak Hours
Off-peak Hours
User A
Peak Hours
Off-peak Hours
User B
Peak Hours
Off-peak Hours
User A
Incentive!!
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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
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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)
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Design Methodology of TDPMethodology
Step 1: Pilot Experiment and Analysis
Baseline Experiment Analysis
QPC Experiment Analysis
Step 2: Empirical User Demand Model Construction
Step 3: Time-of-day Pricing Design Using Game Theoretic Problem
Formulation
Step 4: Network Performance and User Usage Prediction by Simulation
Manager΄s Decision: New Policy Needed?
Measurement Data
Pricing Policy
Control Flow
Data FlowYes
Managed Network
QoS & Metering Router
Internet Access
Meter Reading Server
Web-based Service Management ServerD
BAccounting and
Traffic Control Server
Intranet Traffic
Network Performance Monitoring
No
Manager΄s Review and Adjustment
Network Performance
MonitoringManager’s Decision:
New Policy Needed?
To answer P5): How to design a methodology
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Design Methodology of TDPMethodology
Step 1: Pilot Experiment and Analysis
Baseline Experiment Analysis
QPC Experiment Analysis
Step 2: Empirical User Demand Model Construction
Step 3: Time-of-day Pricing Design Using Game Theoretic Problem
Formulation
Step 4: Network Performance and User Usage Prediction by Simulation
Manager΄s Decision: New Policy Needed?
Measurement Data
Pricing Policy
Control Flow
Data FlowYes
Managed Network
QoS & Metering Router
Internet Access
Meter Reading Server
Web-based Service Management ServerD
BAccounting and
Traffic Control Server
Intranet Traffic
Network Performance Monitoring
No
Manager΄s Review and Adjustment
Step 1: Pilot Experiment and Analysis
Baseline Experiment Analysis
QPC Experiment Analysis
Step 1):Baseline Experiment - No quota limitation - Characterize network problem and user original demandQPC Experiment
- Daily quota- Provide data for constructing user models
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Design Methodology of TDPMethodology
Step 1: Pilot Experiment and Analysis
Baseline Experiment Analysis
QPC Experiment Analysis
Step 2: Empirical User Demand Model Construction
Step 3: Time-of-day Pricing Design Using Game Theoretic Problem
Formulation
Step 4: Network Performance and User Usage Prediction by Simulation
Manager΄s Decision: New Policy Needed?
Measurement Data
Pricing Policy
Control Flow
Data FlowYes
Managed Network
QoS & Metering Router
Internet Access
Meter Reading Server
Web-based Service Management ServerD
BAccounting and
Traffic Control Server
Intranet Traffic
Network Performance Monitoring
No
Manager΄s Review and Adjustment
Step 2: Empirical UserDemand Model Construction
Step 2):Construct myopic and prudent User behavior - Varies with price profile and demand - User preference estimated by QPC experiment
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Design Methodology of TDPMethodology
Step 1: Pilot Experiment and Analysis
Baseline Experiment Analysis
QPC Experiment Analysis
Step 2: Empirical User Demand Model Construction
Step 3: Time-of-day Pricing Design Using Game Theoretic Problem
Formulation
Step 4: Network Performance and User Usage Prediction by Simulation
Manager΄s Decision: New Policy Needed?
Measurement Data
Pricing Policy
Control Flow
Data FlowYes
Managed Network
QoS & Metering Router
Internet Access
Meter Reading Server
Web-based Service Management ServerD
BAccounting and
Traffic Control Server
Intranet Traffic
Network Performance Monitoring
No
Manager΄s Review and Adjustment
Step 3: Time-of-day Pricing Design Using Game-theoretic Problem
Formulation
Step 3):Leader: network manager - Maximize the total bandwidth utilization - keep the total demand below the capacityFollowers: users - Maximize their own benefits
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Design Methodology of TDPMethodology
Step 1: Pilot Experiment and Analysis
Baseline Experiment Analysis
QPC Experiment Analysis
Step 2: Empirical User Demand Model Construction
Step 3: Time-of-day Pricing Design Using Game Theoretic Problem
Formulation
Step 4: Network Performance and User Usage Prediction by Simulation
Manager΄s Decision: New Policy Needed?
Measurement Data
Pricing Policy
Control Flow
Data FlowYes
Managed Network
QoS & Metering Router
Internet Access
Meter Reading Server
Web-based Service Management ServerD
BAccounting and
Traffic Control Server
Intranet Traffic
Network Performance Monitoring
No
Manager΄s Review and Adjustment
Step 4: Network Performance and User Prediction by Simulation
Step 4):Perform numerical assessment based on empirical data under pricing policy by step 3Exploit the experimental data of step 1 and user demand model constructed by step 2 to simulate user behavior.
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Design Methodology of TDPMethodology
Step 1: Pilot Experiment and Analysis
Baseline Experiment Analysis
QPC Experiment Analysis
Step 2: Empirical User Demand Model Construction
Step 3: Time-of-day Pricing Design Using Game Theoretic Problem
Formulation
Step 4: Network Performance and User Usage Prediction by Simulation
Manager΄s Decision: New Policy Needed?
Measurement Data
Pricing Policy
Control Flow
Data FlowYes
Managed Network
QoS & Metering Router
Internet Access
Meter Reading Server
Web-based Service Management ServerD
BAccounting and
Traffic Control Server
Intranet Traffic
Network Performance Monitoring
No
Manager΄s Review and Adjustment
Manager’s ReviewAnd Adjustment
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Myopic and Prudent User Classification
price profile & daily demand
(baseline experiment)
Myopic User Prudent User
}max{/ kBi pQv }min{/ k
Bi pQv
B=Q/min{pk}A=Q/max{pk}0
(myopic)
1 (prudent)
Daily demand
To answer P1):How to exploit empirical data
to model user response w.r.t. price
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Myopic User Model• Focus on short-term benefit maximization
• Maximize i’s own benefit at that time slot k only
kikkiki
vvpvU
ki,, )( Max
,
User preference
volume F(.
) S
atis
fact
ion
diminishing returns of scale
)()( ,,, kikikiki vFvU
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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
K
kttitti
ti
KktvvpvU
ti,,
},...,,{)( Max
,
,, ki
K
kttit Qvp
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How to Estimate Individual User Preference
• Derive preference from optimal conditions
)(/ ,, kikki vFp
QPCkiki vvkiki vF,,,
|)(/1 ,,
User Usage Data under QPC
(pk=1)
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Selection of F(.)
• Myopic user:
• Prudent user:
• User preference:
,*,
k
kiki p
v
Kktp
Qv
t
ti
K
ktji
titi ,...,, ,
,
,*,
QPCkiki v ,,,
Utility(Rate)=log(Rate) Utility(Volume)=log(Volume)
i.e., F(. )=log(. )
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User Volume under Baseline Experiment
User Volume under QPC Experiment
{pk|k=1,2,…,K}
User Classification
Utility Function F(.)=log(. )
User Behavior Model w.r.t. Price
User Preferences
Myopic Prudent
To answer P1):How to exploit empirical data
to model user response w.r.t. price
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TDP Design
• Manager’s Decision Problem:
Price Profile
Maximize total bandwidth utilization of regular service
Total User submission cannot exceed the bandwidth
Goal of Network Manager
When service is free or flat rate
To answer P2): How to design a pricing design
to maximize total bandwidth utilization
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Leader-follower Model
Leader- Network ManagerGoal:
Maximize total bandwidth utilization
Follower- Users
Myopic UserMaximize short-term benefits
Prudent UserMaximize daily benefits
PriceVolume Volume
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Analysis of TDP Policy
• Goals1) How the prices may induce user behavior an
d affect network performance
2) How TDP policy varies w.r.t user behavior
• Problem Settings- 3 users- 3 time units- bandwidth:10 units- price set
Preferences Time Slot 1 Time Slot 2 Time Slot 3
User 1 3 5 7
User 2 5 7 9
User 3 7 9 113,2,1 },5,4,3,2,1{ ipi
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Why Needs User Differentiation
• Case I
– Pricing policy :prudent, Users: myopic • Case II
– Pricing policy : myopic, Users: prudent
Total Submitted Volume (Q=10)
Time Slot 1 Time Slot 2 Time Slot 3
Case I 15 21 13.5
Case II 3.49 3.33 13.03
Submitted volumes are not shaved (>10)
- Bandwidth utilization < 50% at time slots 1 and 2- Congestion happens at time slot 3
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Pricing Policy for Prudent Users
• Hypotheses: – The higher user preference the higher price
for a time slot • Analyses:
– Due to link capacity constraint
BTp
Qv
I
i k
kiK
ktti
kiI
iki
1
,
,
,
1
*,
K
jji
kiki
I
i
kikik W
BT
QWp
1,
,,
1
,, where,
I
i
kikik BT
QWp
1
,,*Total Submitted Volume (Q=10)
Time Slot 1 Time Slot 2 Time Slot 3
QPC Scheme 6.97 10 13.03
TDP Scheme 6.97 10 6.51
• Q=10 P=(1,2,3)• Q=25 P=(2,3,4)
Congestion!
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Pricing Policy for Myopic Users
• The property that no longer holds• Analysis:
– Due to link capacity constraint
*3
*2
*1 ppp
kAi
kik BTp ,
BTp
kAi k
ki
,
kAi
kik BTp ,*
Total Submitted Volume (Q=10)
Time Slot 1 Time Slot 2 Time Slot 3
QPC Scheme 15 21 27
TDP Scheme 7.5 7 7
• Q=10 P=(2,3,1)• Q=20 P=(2,3,3)
Congestion! Not shaved!
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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
Hypotheses1)Optimal price: 2)Drop rate of regular service 0 3)Peak-hour usage:
- Total submitted volume of regular service ↓
- User transmitted volume of Internet access ↓
**peakpeakoff pp
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Peak Shaving and Load Balancing Effects
• Optimal Price 3.1,1.1 ** peakpeakoff pp
0 2 4 6 8 10 12 14 16 18 20 22 240
10
20
30
40
50
60
Day Time
Mbps
Total Submitted Volume under QPC/TDP
Total Submitted Volume under QPC
Drop Rate under QPC/TDPDrop Rate under QPC
Available Bandwidth
Total submitted rate reduced by 11.53% during peak hoursDifference between peak and off-peak hours reduced by 31.21%
Peak-hour drop rate reduced to 0
TDP effectively manages the time-of-day Internet access !
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Peak-hour Abuse Improvement• Abuse Index – Top 5 user Internet usage
QPC Scheme 500MB*2 QPC Scheme
Peak -hours -17.62% -4.4%
0 2 4 6 8 10 12 14 16 18 20 22 241
2
3
4
5
6
7x 10
8
Day Time
Top 5
User
Usage o
f In
tern
et
Access (
Byte
s)
QPC/TDP Scheme
QPC Scheme
35
Peak-hour Fairness Improvement• Fairness Index– Standard deviation of Internet
usage
0 2 4 6 8 10 12 14 16 18 20 22 241
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6x 10
6
Day Time
Sta
ndard
Devia
tion o
f U
ser
Inte
rnet-
Access V
olu
me (
Byte
s)
QPC/TDP Scheme
QPC Scheme
QPC Scheme 500MB*2 QPC Scheme
Peak –hour -17.64% -8%TDP improves peak-hour
abuse and unfairness
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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
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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 p
eak hours
• Designed Quota Scheduling:
peakoff
peakoff
peak
peak
T
IQ
T
IQ
QTT
TQ
peakoffpeak
peakpeak
Q
TT
TQ
peakoffpeak
peakoffpeakoff
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Peak Shaving-based Quota Scheduling (PS-QS)
Off-peak Hours Peak Hours
Total Submission Rate under QPC
Bandwidth Limitation
Estimated User Quota Usage
User Quota Usage Total Submission
d
d
Scheduled Quota
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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
LB-QS PS-QS TDP
LBI (Mbps) 16 9.98 9.28
PSI (Mbps) 57.42 54.75 51.35
• LBI and PSI under PS-QS improved by 37.6% and 4.7% over LB-QS because of considerations on user preferences over time
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)
42
Comparisons of TDP and QS: Total Submission Rate
0 2 4 6 8 10 12 14 16 18 20 22 240
10
20
30
40
50
60
70
80
Day Time
Tot
al S
ubm
issi
on R
ate
of R
egul
ar S
ervi
ce (
Mbp
s)
TDP
PS-QSLB-QS
LINK CAPACITY
•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 )
LB-QS PS-QS TDP
AI (bytes) 226964566 206758615 173572422
FI (bytes) 2631251 2440906 2107364
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Design Related Issues
LB-QS PS-QS TDP
Measurement Requirement
No user data
User data of QPC
User data of QPC and baseline
Calculation Complexity
Simple Simple Solve a leader-follower game
Implementation
Requirement
QS module at accounting and traffic
control server
Pricing module at accounting and traffic
control serverApplicability to
Traffic Pattern
If the peak hours are not contiguous but scattered over all time slots congestion at th
e quota renewal time TDP
0 2 4 6 8 10 12 14 16 18 20 22 240
10
20
30
40
50
60
70
80
Day Time
Tot
al S
ubm
issi
on R
ate
of R
egul
ar S
ervi
ce (
Mbp
s)
TDP
PS-QSLB-QS
LINK CAPACITY
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
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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!