Need of Time-of-day Internet
Access Management
• Peak-hour bandwidth utilization  100%(9
a.m.–3
a.m.)
Q: What
problems do we suffer over
• Peak-hour
drop rate
> 3 rate
Mbpsnetwork?
free-of-charge
or flat
- Ex: NTU dorm
networks
• Peak-hour
usage:
Heavy : normal = 13.08: 1
User
Group
Average Usage
(Bytes)
How to
17,004,173
1) Manage the time-of-day
1GHU
24,530,722
Internet
access
100MHU
2) Design
an incentive
43,410,386 control
scheme NU
4,754,360
2GHU
LU
1,736,643
1
Research on Time-of-day Internet
Access Management by Quotabased Priority Control
Presented by Shao-I Chu
Advisor: Dr. Shi-Chung Chang
Date: June 13, 2007
2
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
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
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)
5
Existing QPC Architecture
DB
Web-based Service
Management Server
Accounting and
Traffic Control Server NTU Domain
Merits of QPC
congestion Router
improved
48%
QoS Routerby54Mbps
(packet engine 5022)
- Over 91% users’
usage encouraged
54Mbps Metering Router
Router
Weakness
of
QPC
(Cisco 7513)
(Cabletron SSR2000)
- No consideration on temporal effect
Meter Reading
Server
Dormitory
- Daily
Network
TANet
Dormitory
Network
6
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
7
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
7
x 10 7
5 x 10
8
4.5
7
4
Internet Usage (Bytes)
Internet Usage (Bytes)
6
3.5
5
3
2.5
4
2
3
1.5
2
1
1
0.5
0
0
0
0
8
2
2
4
4
66
88
10
12
14
10
12
14
Day
DayTime
Time
16
16
18
18
20
20
22
22
24
24
How to Design the
Management Schemes
M1) How to effectively manage the time-ofday 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
9
Design of Management
Schemes
••Quota
Scheduling
Virtual
Pricing
price=number
quota perfor
byte
––Different
quota of
allocations
different time
–periods
Price varies with time
Ex: Time length: peak: off-peak=1:1
Off-peak
Hours Off-peak
Hours
Off-peak Off-peak
Hours Hours
Peak Peak
HoursHours
Incentive!!
User A User A
Peak
Hours
User B
Peak
Hours
User B
10
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
11
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
12
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
To answer P3) and P4) 
P3) How
to
design
a
simple
pricing
policy
for
Static Time-of-day Pricing (TDP)
user acceptance
P4) How to exploit the existing hardware and
software of the legacy network
P5) How to design a methodology
13
Design Methodology of TDP
Network
Manager΄s Decision:
Performance
Manager’s
Decision:
New Policy Needed? Monitoring
Methodology
No
Network
Performance
Monitoring
Measurement Data
New Policy Needed?
Control Flow
Yes
Data Flow
Step 1: Pilot Experiment and Analysis
To answer P5):
How to design a methodology
Baseline Experiment
Analysis
QPC Experiment
Analysis
Step 2: Empirical User Demand Model
Construction
Web-based Service
Management Server
D
B
Accounting and
Traffic Control Server
Step 3: Time-of-day Pricing Design
Using Game Theoretic Problem
Formulation
Managed Network
Meter Reading
Server
Internet Access
QoS & Metering Router
Intranet Traffic
Step 4: Network Performance and User
Usage Prediction by Simulation
Manager΄s Review and
Adjustment
Pricing Policy
14
Design Methodology of TDP
Methodology
No
Network
Performance
Monitoring
Measurement Data
Manager΄s Decision:
New Policy Needed?
Step 1: Pilot Experiment and Analysis
Control Flow
Yes
Data Flow
Step
1):
Baseline Experiment
QPC Experiment
Baseline Experiment
Analysis
Analysis
Step 1: Pilot Experiment and Analysis
Baseline Experiment
Analysis
QPC Experiment
Analysis
- No quota limitation
- Characterize network problem
and user original demand
Step 2: Empirical User Demand Model
Construction
D
B
Web-based Service
Management Server
Accounting and
Traffic Control Server
Step 3: Time-of-day Pricing Design
Using Game Theoretic Problem
Formulation
QPC Experiment
Managed Network
Meter Reading
Server
Internet Access
QoS & Metering Router
Intranet Traffic
- Daily quota
- Provide data for constructing user models
Step 4: Network Performance and User
Usage Prediction by Simulation
Manager΄s Review and
Adjustment
Pricing Policy
15
Design Methodology of TDP
Methodology
No
Network
Performance
Monitoring
Measurement Data
Manager΄s Decision:
New Policy Needed?
Control Flow
Yes
Data Flow
Step 2):
Construct myopic and prudent
Step 2: Empirical User
User behavior
Demand Model Construction
Step 1: Pilot Experiment and Analysis
Baseline Experiment
Analysis
QPC Experiment
Analysis
Step 2: Empirical User Demand Model
Construction
Web-based Service
Management Server
D
B
- Varies with price profile and demand
- User preference estimated by QPC experiment
Accounting and
Traffic Control Server
Step 3: Time-of-day Pricing Design
Using Game Theoretic Problem
Formulation
Managed Network
Meter Reading
Server
Internet Access
QoS & Metering Router
Intranet Traffic
Step 4: Network Performance and User
Usage Prediction by Simulation
Manager΄s Review and
Adjustment
Pricing Policy
16
Design Methodology of TDP
Methodology
No
Network
Performance
Monitoring
Measurement Data
Manager΄s Decision:
New Policy Needed?
Control Flow
Yes
Step 3):
Leader: network manager
Data Flow
Step 1: Pilot Experiment and Analysis
Baseline Experiment
Analysis
QPC Experiment
Analysis
- Maximize the total bandwidth utilization
- keep the total demand below the capacity
Step 2: Empirical User Demand Model
Construction
Web-based Service
Management Server
D
B
Step
3
: Time-of-day Pricing Design
Followers: users
Accounting and
Traffic Control Server
3: Time-of-day Pricing Design
UsingStep
Game-theoretic
Problem
Using
Game Theoretic Problem
Formulation
- Maximize
their own benefits
Formulation
Managed Network
Meter Reading
Server
Internet Access
QoS & Metering Router
Intranet Traffic
Step 4: Network Performance and User
Usage Prediction by Simulation
Manager΄s Review and
Adjustment
Pricing Policy
17
Design Methodology of TDP
Methodology
No
Network
Performance
Monitoring
Measurement Data
Manager΄s Decision:
New Policy Needed?
Control Flow
Yes
Data Flow
Step 1: Pilot Experiment and Analysis
Step 4):
Baseline Experiment
Analysis
QPC Experiment
Analysis
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
userPerformance
behavior.
Step
4: Network
and
Step 2: Empirical User Demand Model
Construction
Web-based Service
Management Server
D
B
Accounting and
Traffic Control Server
Step 3: Time-of-day Pricing Design
Using Game Theoretic Problem
Formulation
Managed Network
Meter Reading
Server
Internet Access
QoS & Metering Router
Intranet Traffic
Step 4: Network Performance and User
Prediction by Simulationby Simulation
UserUsage
Prediction
Manager΄s Review and
Adjustment
Pricing Policy
18
Design Methodology of TDP
Methodology
No
Network
Performance
Monitoring
Measurement Data
Manager΄s Decision:
New Policy Needed?
Control Flow
Yes
Data Flow
Step 1: Pilot Experiment and Analysis
Baseline Experiment
Analysis
QPC Experiment
Analysis
Step 2: Empirical User Demand Model
Construction
Web-based Service
Management Server
D
B
Accounting and
Traffic Control Server
Step 3: Time-of-day Pricing Design
Using Game Theoretic Problem
Formulation
Managed Network
Meter Reading
Server
Internet Access
QoS & Metering Router
Intranet Traffic
Step 4: Network Performance and User
Usage Prediction by Simulation
Manager’s Review
Review and
AndManager΄s
Adjustment
Adjustment
Pricing Policy
19
Myopic and Prudent User
Classification
price profile &
daily demand
To answer P1):
(baseline experiment)
How to exploit empirical data
B
B
v
vi  Q / max{
i  Q / min{ pk }
to pmodel
user response w.r.t. price
k}
1 (prudent)
Prudent
Daily demandUser
Myopic User
0 (myopic)
A=Q/max{pk}
B=Q/min{pk}
20
Myopic User Model
• Focus on short-term benefit maximization
• Maximize i’s own benefit at that time slot k
only
Max U ik (vi ,k )  pk vi ,k
vi ,k
U ik (vi ,k )  i ,k F (vi ,k )
F(.) Satisfaction
User preference
diminishing returns of scale
volume
21
Prudent User Model
• Focus on daily benefit maximization
• Maximize i’s total benefit from time slot k
to time slot K
 U
K
Max
{vi , t ,t  k ,..., K }
t k
t
i
(v i ,t )  p t v i ,t

subject to the quota budget constraint
K
pv
t k
t i ,t
Qi ,k
22
How to Estimate Individual
User Preference
• Derive preference from optimal conditions
i ,k  p k / F (vi ,k )
User Usage Data
under QPC
(pk=1)
i,k  1/ F (vi,k ) |vi ,k vi ,k ,QPC
23
Selection of F(.)
• Myopic user:
v 
*
i ,k
i ,k
pk

• PrudentUtility(Rate)=log(Rate)
user:
 i ,t Qi ,t
Utility(Volume)=log(Volume)
*
v i ,t  K
, t  k ,..., K
pt
i.e., F(. )=log(.
)

 i, j
t k
• User preference:
i ,k  vi ,k ,QPC
24
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 Behavior Model w.r.t. Price
Myopic
Prudent
User Preferences
User Volume under
QPC Experiment
Utility Function F(.)=log(.
25 )
TDP Design
• Manager’s Decision Problem:
To answer P2):
Price
How to design
a pricing design
Profile
to maximize total
bandwidth utilization
Goal
of Network Manager
When
service
free orutilization
flat rate
Maximize
total is
bandwidth
of regular service
Total User submission
cannot exceed the bandwidth
26
Leader-follower Model
Leader- Network Manager
Goal:
Maximize total bandwidth utilization
Volume
Price
Volume
Follower- Users
Myopic User
Maximize short-term benefits
Prudent User
Maximize daily benefits
27
Analysis of TDP Policy
• Goals
1) How the prices may induce user behavior
and affect network performance
2) How TDP policy varies w.r.t user behavior
• Problem Settings
- 3 users
Preferences Time Slot 1 Time Slot 2 Time Slot 3
- 3 time units
User 1
3
5
7
- bandwidth:10
units
User
2
5
7
9
- price
set
pi User
{1,2,3,4,5}, i  17,2,3
9
11
28
Why Needs
User Differentiation
• Case I
–Pricing policy :prudent, Users: myopic
• Case II
–Pricing policy : myopic, Users: prudent
Submitted volumes are not shaved (>10)
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
- Bandwidth utilization < 50% at time slots 1 and
2
29
- Congestion happens at time slot 3
Pricing Policy for Prudent
Users
• Hypotheses:
– The higher user preference  the higher price
for a time slot
• Analyses:
– Due to link capacity constraint



I W Q 
I
I
Total Submittedv* p*Time
1ii ,,kk  Time
Slot 2
i , ki , k Q
 Slot


BT

i ,k k 
K

pk 
•
Q=10

P=(1,2,3)
Volume (Q=10)
BT
i 1
i 1 i 1
  i ,t

t
k

P=(2,3,4)
QPC Scheme• Q=256.97
10

Time Slot 3
Congestion!
Wi ,k Qi ,k
i , k
TDP Scheme
pk   6.97 , where Wi ,k 10
 K
BT
i 1
I

j 1
i, j
13.03
6.51
30
Pricing Policy for Myopic
Users
p1*  p 2*  p3* no longer holds
• The property that
• Analysis:
– Due to link capacity constraint

iAk
i ,k
pk
 BT p 
*
k
i ,k
 BT
pk 
iAk
 i ,k
 BT
i Ak
Total Submitted Time Slot 1 Time Slot 2 Time Slot 3
Volume (Q=10)• Q=10  P=(2,3,1) Congestion! Not shaved!
QPC Scheme• Q=2015 P=(2,3,3)
21
27
TDP Scheme
7
7.5
7
31
Effectiveness Evaluation
• Parameter
Hypotheses
Setting
*
*
p

p
1)Optimal
off 3
peak
– Peak
hour price:
9 a.m. to
a.m peak
2)Drop
rate of regular
service
 a.m.
0
– Quota
replenishment
point
 6:00
3)Peak-hour usage:
– Length of each time slot 10 minutes.
- Total submitted volume of regular
– Bottleneck
bandwidth
 54Mbps.
service
↓
– Admissible
price
set (per byte):
- User
transmitted
volume of Internet
Ω={1, 1.1, access
1.2, 1.3, ↓1.4, 1.5}
– Quota budget of each user  1G
32
Peak Shaving and Load
Balancing Effects
*
*
poff

1
.
1
,
p
 peak
peak  1.3
• Optimal Price 
60
Mbps
50
Total submitted rate reduced by 11.53%
40
during peak hours
TDPDifference
effectively
manages
between peak and off-peak
30
the time-of-day
Internet
hours reduced
by 31.21%access !
Total Submitted Volume under QPC/TDP
Total Submitted Volume under QPC
Drop Rate under QPC/TDP
Drop Rate under QPC
Available Bandwidth
20
Peak-hour drop rate
reduced to 0
10
0
0
2
4
6
8
10
12
14
Day Time
16
18
20
22
24 33
Peak-hour Abuse Improvement
• Abusex 10Index – Top 5 user Internet usage
8
Top 5 User Usage of Internet Access (Bytes)
7
QPC/TDP Scheme
QPC Scheme
6
5
QPC Scheme
500MB*2 QPC Scheme
-17.62%
-4.4%
Peak -hours
4
3
2
1
0
2
4
6
8
10
12
14
Day Time
16
18
20
22
24
34
Peak-hour Fairness Improvement
• Fairness Index– Standard deviation of Internet
usage
Standard Deviation of User Internet-Access Volume (Bytes)
6
6
x 10
5.5
QPC/TDP Scheme
QPC Scheme
5
QPC Scheme
500MB*2 QPC Scheme
TDP improves peak-hour
Peak4 –hour
-17.64%
-8%
abuse and unfairness
4.5
3.5
3
2.5
2
1.5
1
0
2
4
6
8
10
12
14
Day Time
16
18
20
22
24
35
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
36
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
37
Load Balancing-based
Quota Scheduling (LB-QS)
• Objective:
– Equalize the average traffic of peak and off
peak hours
IQ peak
Tpeak

IQoff  peak
Toff  peak
• Designed Quota Scheduling:
Qpeak 
Tpeak
Tpeak  Toff  peak
Q
Qoff  peak 
Toff  peak
Tpeak  Toff  peak
Q
38
Peak Shaving-based Quota
Scheduling (PS-QS)
Total Submission Rate under QPC
d
Bandwidth Limitation
User Quota Usage  Total Submission
d
Off-peak Hours
Estimated User Scheduled
Quota Usage
Quota
Peak Hours
39
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
40
Comparisons of TDP and QS:
Load Balancing & Peak Shaving
• Peak Shaving Index (PSI):
Evaluated
empirical
data
of NTU
dormitory
– Averageover
total the
submission
rate
of peak
hours
network
• Load Balancing Index (LBI)
– Difference
total submission
LB-QS
(Qpeakof
,Qaverage
250MB)rates
off-peak)=(750MB,
between peak and off-peak hours
No user usage data needed
LB-QS
PS-QS 380MB)
TDP
PS-QS (Qpeak,Qoff-peak
)=(620MB,
LBIusage
(Mbps) data
16of QPC
9.98
9.28
User
PSI
(Mbps) 57.42
54.75
51.35
TDP (P
peak,Poff-peak)=(1.3, 1.1)
User usage data of QPC
• LBI Baseline
and PSI under
improved by 37.6% and 4.7%
dataPS-QS
(no control)
over LB-QS because of considerations on user preferences
41
over time
Comparisons of TDP and QS:
Total Submission Rate
Total Submission Rate of Regular Service (Mbps)
80
TDP
PS-QS
LB-QS
LINK CAPACITY
70
60
50
40
30
•a spike
(congestion) at 9 a.m. because of no
20
price and no user differentiation
10
•PS-QS encourages more usage than LB-QS
0
because
of user
0
2
4
6 preference
8
10
12
14
16
18
20
22
24
Day Time
42
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
AI (bytes)
FI (bytes)
LB-QS
226964566
2631251
PS-QS
206758615
2440906
TDP
173572422
2107364
• TDP outperforms QS by at least 14%
• PS-QS is better than LB-QS (user preferences )
43
Design Related Issues
LB-QS
PS-QS
Measurement
Requirement
Calculation
Complexity
Implementation
Requirement
Total Submission Rate of Regular Service (Mbps)
80
TDP
TDP
PS-QS
LB-QS
LINK CAPACITY
70
No user User data of
data
QPC
Simple
Simple
User data of QPC
and baseline
Solve a leaderfollower game
60
50
40
30
20
QS module at
Pricing module at
accounting and traffic accounting and traffic
control server
control server
Applicability to
If the peak hours are not contiguous but
Traffic
scattered over all time slots  congestion
Pattern
at the quota renewal time TDP
10
0
0
2
4
6
8
10
12
14
Day Time
16
18
20
22
24
44
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
45
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
46
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
47
Thanks for your attention!
48