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Transcript of Case Studies & Network Planning Tools
TESRG
Tutorial : Planning of IP-based Networks
Case Studies &
Network Planning Tools
Dr.-Ing. Eueung Mulyana ST. MSc. Telecommunication Engineering Scientific and Research Group
School of Electrical Engineering and Informatics Institut Teknologi Bandung
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Outline
Survey
Current ISP networks and REN (Research and Education Network)
Some network planning tools
Case Study
An ISP experience
Network routing and dimensioning
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RENs and some Tier-1 ISP Networks
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Abilene
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GEANT
Collaboration between 26 RENs (30 countries – 30/34 GEANT2) and the EU commission
Multi gigabit pan-european data communication networks, specifically for RE use
Multiple 10Gbps wavelength in the core; 44 links
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XWiN
X.25-WiN 1989/1990 (64Kbps core)
B-WiN 1995 (IP/ATM)
G-WiN 2000 (IP/SDH)
XWiN
The 4-th generation of the german REN
45 core nodes; 500 universities and research institutes
Multiple gigabit core; DWDM technology
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JaNET
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SingAREN
Singapore Advance Research and Education Networks (from 1997)
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Sprint
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MCI/Verizon
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Network Planning and Optimization Tools
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Cariden
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Cariden
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TOTEM
TOolbox for Traffic Engineering Methods
Open-source
Maintained by Olivier Bonaverture, Bernard Fortz (Belgium)
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WANDL
Network Planning and Analysis Tools (NPAT) used e.g. by Global Crossing
IP/MPLSView
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Infosim
Performance management
Fault and event management
MPLS optimization
Configuration management
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Case Study: QoS Deployment at Global Crossing
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Providing QoS in the Backbone
QoS Problems:
Non-network-related
Network-related
Overloaded Server
Operation Errors (eg. Router/switch misconfig.)
Upgrade Adding new servers + load balancing
Deploying OSS (Operation Support Systems)
Equipment problems (hw/sw)
Lack of access capacity
Testing & Troubleshooting
Adding capacity; Classification and different treatment
Uneven traffic distribution Routing control
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Global Crossing‘s Experiences
Providing QoS may not make economic sense if users are not willing to pay for it
4 Strategic actions:
Good network design (incl. regular clean-up)
Prevent QoS problems from happening
Checking & solve failure, bottlenecks Capacity upgrade (incl. failure cases) Routing re-evaluation Examining logs & security measures
Deploying DiffServ
Premium (VoIP, Video Conference, Financial and network control traffic)
Assured (non real-time VPNs)
Best-Effort
1
2
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Global Crossing‘s Experiences (Cnt‘d)
Deploying MPLS
Two LSPs for each i-LSR and e-LSR: one for Premium with Fast Reroute enabled; and one for assured and BE traffic
Depending on network policy and billing models
Backup LSPs can be pre-configured; hot standby or instantly computed at the event of failures
Performing class-based queueing, scheduling, policing and shaping
3
4
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Case Study: Network Routing and Dimensioning
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A Dimensioning Problem
Traffic Matrix (Mbps)
1
3
2
4
5
- 50 40
90
- 90
- -
-
1 2 4
1
2
3
70
70
-
3
90
40
30
5
- - - 4 - 100
- - - 5 - -
Design data:
Two types of transport modules STM1 & STM4
Cost ratio (STM4/STM1) 2.5
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- d=1
-
- -
-
1 2 4
1
2
3 -
3 5
- - - 4 -
- - - 5 - -
d=2 d=3 d=4
d=5 d=6 d=7
d=8 d=9
d=10
d – node pairs relation
Formulation
Unit cost of transport module t
Number of transport modules installed on link e
Over-provisioning requirement
Capacity of transport module t
Load fraction of demand d; routed through path p
Volume of demand d; traffic class
Load fraction (normalized) of demand d; routed through path p
Link-path incidence matrix
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1
3 2
4
5
1
3 2
4
5
1
3 2
4
5
1
3 2
4
5
1
3 2
4
5
Routing Possibilities
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1
3 2
4
5
1
3 2
4
5
1
3 2
4
5
1
3 2
4
5
1
3 2
4
5
Routing Possibilities (Cnt‘d)
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d=1 d=2 d=3 d=4 d=5 d=6 d=7 d=8 d=9 d=10
p=1
p=2
p=3
1
0
-
0
1
-
0
1
0
0
1
-
1
0
-
0
1
-
1
0
-
0
1
-
0
0
-
0
1
-
Incidence Matrix
d=1 d=2 d=3 d=4 d=5 d=6 d=7 d=8 d=9 d=10
p=1
p=2
p=3
0
1
-
1
0
-
1
0
0
0
0
-
1
0
-
0
1
-
0
0
-
0
1
-
1
0
-
0
0
-
d=1 d=2 d=3 d=4 d=5 d=6 d=7 d=8 d=9 d=10
p=1
p=2
p=3
0
0
-
0
0
-
0
0
1
1
0
-
0
0
-
0
0
-
1
0
-
0
0
-
1
0
-
0
1
-
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Incidence Matrix (Cnt‘d)
d=1 d=2 d=3 d=4 d=5 d=6 d=7 d=8 d=9 d=10
p=1
p=2
p=3
0
1
-
0
1
-
0
1
0
0
1
-
0
1
-
1
0
-
0
1
-
0
1
-
0
0
-
0
1
-
d=1 d=2 d=3 d=4 d=5 d=6 d=7 d=8 d=9 d=10
p=1
p=2
p=3
0
1
-
0
1
-
1
0
0
0
0
-
0
1
-
0
1
-
0
0
-
1
0
-
0
1
-
0
0
-
d=1 d=2 d=3 d=4 d=5 d=6 d=7 d=8 d=9 d=10
p=1
p=2
p=3
0
0
-
0
0
-
0
0
1
0
1
-
0
0
-
0
0
-
0
1
-
0
0
-
0
1
-
1
0
-
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Using an LP Solver: lpsolve
minimize z = x1 + x2 , where x1 integer
subject to:
221
12
11
xx
x
x
min: x1 + x2;
x1 > 1;
x2 > 1;
x1 + x2 > 2;
int x1;
lp_solve < data1.lp > data1.out
The value of objective function: 2
x1 1
x2 1
Problem
Formulation (data1.lp)
Run lp_solve
Output (data1.out)
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LPSolve IDE
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LPSolve IDE (2)
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Writing the Input Files
Unit cost of transport module t
Number of transport modules installed on link e
t=1 STM1
t=2 STM4
y_1_1 + 2.5 y_1_2 +
y_2_1 + 2.5 y_2_2 +
y_3_1 + 2.5 y_3_2 +
y_4_1 + 2.5 y_4_2 +
y_5_1 + 2.5 y_5_2 +
y_6_1 + 2.5 y_6_2;
Min:
y_e_t
for this example distance independent
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Writing the Input Files (2)
Load fraction (normalized) of demand d; routed through path p
PC1: u_1_1 + u_1_2 = 1;
PC2: u_2_1 + u_2_2 = 1;
PC3: u_3_1 + u_3_2 + u_3_3 = 1;
PC4: u_4_1 + u_4_2 = 1;
PC5: u_5_1 + u_5_2 = 1;
PC6: u_6_1 + u_6_2 = 1;
PC7: u_7_1 + u_7_2 = 1;
PC8: u_8_1 + u_8_2 = 1;
PC9: u_9_1 + u_9_2 = 1;
PC10: u_10_1 + u_10_2 = 1;
u_d_p
Constraint‘s name (optional)
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Writing the Input Files (3)
Volume of demand d; traffic class
Load fraction of demand d; routed through path p
DP1: x_1_1 = 50 u_1_1;
DP2: x_1_2 = 50 u_1_2;
DP3: x_2_1 = 70 u_2_1;
DP4: x_2_2 = 70 u_2_2;
DP5: x_3_1 = 40 u_3_1;
DP6: x_3_2 = 40 u_3_2;
DP7: x_3_3 = 40 u_3_3;
DP8: x_4_1 = 90 u_4_1;
DP9: x_4_2 = 90 u_4_2;
DP10: x_5_1 = 70 u_5_1;
DP11: x_5_2 = 70 u_5_2;
DP12: x_6_1 = 90 u_6_1;
DP13: x_6_2 = 90 u_6_2;
DP14: x_7_1 = 40 u_7_1;
DP15: x_7_2 = 40 u_7_2;
DP16: x_8_1 = 90 u_8_1;
DP17: x_8_2 = 90 u_8_2;
DP18: x_9_1 = 30 u_9_1;
DP19: x_9_2 = 30 u_9_2;
DP20: x_10_1 = 100 u_10_1;
DP21: x_10_2 = 100 u_10_2;
- 50 40
90
- 90
- -
-
1 2 4
1
2
3
70
70
-
3
90
40
30
5
- - - 4 - 100
- - - 5 - -
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Writing the Input Files (4) Over-provisioning
requirement
Capacity of transport module t
Load fraction of demand d; routed through path p
CC1: 2 x_1_1 + 2 x_2_2 + 2 x_3_2 + 2 x_4_2 + 2 x_5_1 +
2 x_6_2 + 2 x_7_1 + 2 x_8_2 + 2 x_10_2 < 155 y_1_1 + 620 y_1_2;
CC2: 2 x_1_2 + 2 x_2_1 + 2 x_3_1 + 2 x_5_1 + 2 x_6_2 +
2 x_8_2 + 2 x_9_1 < 155 y_2_1 + 620 y_2_2;
CC3: 2 x_3_3 + 2 x_4_1 + 2 x_7_1 + 2 x_9_1 + 2 x_10_2 < 155 y_3_1 + 620 y_3_2;
CC4: 2 x_1_2 + 2 x_2_2 + 2 x_3_2 + 2 x_4_2 + 2 x_5_2 +
2 x_6_1 + 2 x_7_2 + 2 x_8_2 + 2 x_10_2 < 155 y_4_1 + 620 y_4_2;
CC5: 2 x_1_2 + 2 x_2_2 + 2 x_3_1 + 2 x_5_2 + 2 x_6_2 +
2 x_8_1 + 2 x_9_2 < 155 y_5_1 + 620 y_5_2;
CC6: 2 x_3_3 + 2 x_4_2 + 2 x_7_2 + 2 x_9_2 + 2 x_10_1 < 155 y_6_1 + 620 y_6_2;
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Writing the Input Files (5)
Single Path Routing
Discrete Capacity
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The Result
Objective Value
Variable values
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Routing Decisions
1
3 2
4
5
1
3 2
4
5
1
3 2
4
5
1
3 2
4
5
1
3 2
4
5
u_1_1 = 1
u_1_2 = 0
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1
3 2
4
5
1
3 2
4
5
1
3 2
4
5
1
3 2
4
5
1
3 2
4
5
Routing Decisions (Cnt‘d)
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1
3
2
4
5
Installed Links
STM1
STM4
y_1_1 = 0
y_1_2 = 1
y_5_1 = 0
y_5_2 = 0
Results:
4 STM4 Lines; 1 STM1 Line
If base-cost for leasing one STM1 line is IDR 20 Jt/year/km (IDR 2M/year/100 km) total cost necessary for carrying demands IDR 220 jt/year/km (22 M/year)
Our planning activities can save IDR 80/jt/year/km (8 M/year) compared to if we install 1 STM4 on each link
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1
3
2
4
5
1
3
2
4
5
Load Distribution
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Using A Heuristic Approach (A Slightly Different Routing Scheme)
cost = 0;
For all E1 demands
Determine all possible routes;
For all possible routes
Calculate cost(r)=additional leased line needed on route r;
End for
Select route r with lowest cost;
Add further STM1 or STM4 if necessary;
Establish E1 channel on the chosen route r;
cost = cost+cost(r);
End for
A Greedy Heuristic:
SDH Network dimensioning
Traffic matrix in E1 channel granularity
Developed at TUHH Germany
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GUI
Traffic Matrix (E1 Granulariy)
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GUI (2)
Cost distribution
Network map
Control
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GUI (3)
demand on a certain link
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Greedy Heuristic Global Optimization
General Comparison
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Thank You !
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