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

TESRG Case Studies & Tools Eueung Mulyana

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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


3

RENs and some Tier-1 ISP Networks


4

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


25

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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Volume of demand d; traffic class


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


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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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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