Dimensioning of IP Backbone

TESRG

Tutorial : Planning of IP-based Networks

Dimensioning of IP Backbone

Dr.-Ing. Eueung Mulyana ST. MSc. Telecommunication Engineering Scientific and Research Group

School of Electrical Engineering and Informatics Institut Teknologi Bandung

TESRG Dimensioning of IP Backbone Eueung Mulyana

2

Outline

Introduction : the Internet and information transfer process

Routing in IP networks

Traffic engineering, network dimensioning and planning

Basics of network dimensioning

Optimization Approaches

Dimensioning of IP networks


3

The Internet & Information Transfer


4

The Internet

Global Crossing

UUNet

AI7 IM7

NJIT

MIT

Cimahi-Net

Telekomnet GarutNet

AS


5

Tier-1 ISP

The Internet (Cnt‘d)

Tier-1 ISP

Tier-1 ISP

NAP

National/ Regional ISP

National/ Regional ISP

Local ISP

Local ISP

NAP/ IXP


6

The Internet (Cnt‘d)

AR

BR CR

CR

HR

BR CR

CR

HR

CR CR

AR

HR

CR

CR

AR

Peering (cust. ISP) links

Access links

PoP

Cimahi-Net


7

Application

Information Transfer

1011011011

1001110001

1011111001

1100111000

1110101010

: : :

Transport

Network

Link

Network

Link

Network

Link

Application

Transport

Network

Link

Host Web-Server

Router ISP

Host Home-Computer

Router ISP

.......

index.html file1.jpg file2.jpg ........

Web-Browser


8

Information Transfer (Cnt‘d)

Application

1011011011

1001110001

1011111001

1100111000

1110101010

: : :

Transport

Network

Link

1100111000

110011 1000

1100111000

110011 1000

eg. TCP Packets

IP Packets

eg. Ethernet


9

Routing in IP Networks


10

Routing in the Internet

Terms

Inter-domain vs. Intra-domain

Explicit vs. Hop-by-hop

Distributed vs. Centralized

Link-state vs. Distance-vector

Internet Routing

EGP (Exterior Gateway Protocol)

IGP (Interior Gateway Protocol)

Policy-based: BGP Metric-based: OSPF, IS-IS


11

Intra-Domain Routing: Shortest Path Routing (Unique) – (1)

1 2

3 4

5 6

1

2

2

2

5

5 3

1

2 3

4 5

6

Dest. Next hop Interface

2 direct 1-2

3 direct 1-3

4 3 1-3

5 3 1-3

6 3 1-3


12


1 3 4-3

3 direct 4-3

5 3 4-3

2 direct 4-2

6 direct 4-6


1 direct 3-1

4 direct 3-4

5 direct 3-5

2 1 3-1

6 4 3-4


2 direct 1-2

3 direct 1-3

4 3 1-3

5 3 1-3

6 3 1-3

Intra-Domain Routing: Shortest Path Routing (Unique) – (2)

1 2

3 4

5 6

6

5

6 5

5 3 1-3

5 direct 3-5

6

6

6

5

5


13

Intra-Domain Routing: Shortest Path Routing (ECMP) – (1)

1 2

3 4

5 6

1

1

1

1

1

1 1

1

2 3

4 5

6


2 direct 1-2

3 direct 1-3

4 2 1-2

5 3 1-3

6 2 1-2

3 1-3

3 1-3


14

Intra-Domain Routing: Shortest Path Routing (ECMP) – (2)

1 2

3 4

5 6

6

6

6

6

6

6

6

6


2 direct 1-2

3 direct 1-3

4 2 1-2

5 3 1-3

6 2 1-2

3 1-3

3 1-3

Router 1

6

6

6

6

6 2 1-2

3 1-3

6

6


1 direct 2-1

4 direct 2-4

3 1 2-1

6 4 2-4

5 1 2-1

4 2-4

4 2-4

Router 2

6

6


1 direct 3-1

4 direct 3-4

2 1 3-1

5 direct 3-5

6 4 3-4

4 3-4

5 3-5

Router 3

6

6

6


2 direct 4-2

3 direct 4-3

1 2 4-2

6 direct 4-6

5 3 4-3

3 4-3

6 4-6

Router 4

6


1 3 5-3

3 direct 5-3

2 3 5-3

6 direct 5-6

4 3 5-3

6 5-6

6 5-6

Router 5


15

Vanilla LSP

ER LSP

2

1 2

3 5

2

5

1 2

3 4

5 6

Link Weights

1

2 3

4 5

6

1 2

3 4

5 6

MPLS allows explicit (using ER-LSPs) other than shortest path routing (using Vanilla LSPs)

DiffServ gives possibility to differentiate treatements for IP packets with respect to their class of service e.g. class-based routing

MPLS and DS-MPLS


16

Traffic Engineering, Network Dimensioning & Planning


17

2.5 Gbps 2.5 Gbps

not optimized optimized

8.0

5.0

saving 30% of the capacity

30% from 2.5 Gbps = 750 Mbps

> 11000 64kbps channels

> 1.1 Mio EURO/month (100 EURO/channels/month)

Why ?

Money :

To save money !!

To earn more !!

To control and manage resources !!

To increase performance, efficiency !

The Need


18

A new Telco Company

Customers

Partners, Providers, Vendors

Demands

Cost Model

Management Reqs./Cons.

The Need (Cnt‘d)


19

Why ?

Not trivial, large scale !!

Mathematical Tools

CPLEX

Xpress-MP

OSL

Customized Tools

Genetic Algorithms

Simulated Annealing

Neural Networks

G-WiN(2002) SURFnet5(2002)

#constraints = 6340 (122 pages !!)

#variables = 3601

(69 pages)

#constraints = 42818 (823 pages !!)

#variables = 23257

(447 pages)

How and Why ?


20

Dago

Buah-Batu

Cimahi

1

3

2

Source : http://telecom.ee.itb.ac.id/~tutun/ET3042/

Assumptions:

Fixed / direct routing

Mean holding time = 3 mins

Traffic distribution as given in the traffic matrix

Tasks:

Node dimensioning s.t. load utilization less than 50 %

Link dimensioning s.t. blocking less than 1 %

60 15 15

15

15 30

30 30

30

1 2 3

1

2

3

Traffic Matrix (erlang)

Telephone Networks


21

Link Dimensioning Number

of channel Integer

Erlang B formulae

Blocking

Offered traffic

Given:

Traffic Matrix

Required:

Number of channels for each link ( )

Solution:


22

IP Networks

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


23

1

3

2

4

5

1

3

2

4

5

STM1

STM4

IP Networks (Cnt‘d)


24

Basics of Network Dimensioning


25

Transmission Channels

DS-1/T-1 E-1 1.5 Mbps 2 Mbps

DS-3/T-3 45 Mbps E-3 35 Mbps

OC-1 52 Mbps

OC-3 155 Mbps STM-1

OC-12 622 Mbps STM-4

OC-48 2.5 Gbps STM-16

OC-192 10 Gbps STM-64

OC-768 40 Gbps STM-256


26

Optimization Algorithm

Input (data, parameters,etc.)

Constraints Objective(s)

Output

A System View


27

ILP

(Integer Linear Programming)

Input (data, parameters,etc.)

Constraints Objective(s)

Output

• traffic matrix (demand between node-pairs) • type of transmission facilities and their cost parameters • set of paths for all node-pairs • topology

flow constraint (bifurcation is allowed) link capacity constraint

•

•

minimize the cost for installing transmission facilities

•

type and number of transmission facility to be installed on each link in the network total cost needed to construct the network routing of the demands

•

•

•

A System View (Cnt‘d)


28

Basic sets : N (set of nodes), E (set of edges), K (set of demands)

T (set of transmission facility types)

Constants and parameters :

– amount of traffic demand k K, between nodes sk and dk

– cost of using transmission facility t T on edge {i,j} E

– capacity of transmission facility t T

Sets :

– set of feasible paths of traffic demand k K

– set of feasible paths of traffic demand k K that use edge {i,j}

Variables :

– real variable indicating how much of traffic demand k K goes

through feasible path p Pk

– integer variable indicating how many transmission facility t T

should be installed on edge {i,j} E

k

pf

k

ijP }{

kb

kP

t

ijc

}{

t

t

iju

}{

Basic Notations


29

1

2

3

4

5

N={1,2,3,4,5} E={1,2,3,4,5,6} K={1,2} T={1} single facility type t=1==16

sk dk bk k

1

2

1 4 20

5 3 12

1

2

3

4

5 1

2

3

4

5

Pk=1={1,2,3}

Pk=2={1,2}

Plain Text


30

0.8,0.4,0.81

3

1

2

1

1 fff

1

2

3

4

5

1

2

3

4

5

8.0

8.0

4.0

12.0

8.0

8.0

3,2,3,3

2,1,1

1

}24{

1

}23{

1

}13{

1

}12{

1

}45{

1

}15{

PPP

PPP

Kkbf k

Pp

k

p

k

,

Pk=1={1,2,3}, |P1|=3

The sets representing the feasible paths are: Consider traffic demand k = 1:

sk dk bk k

1 1 4 20

The variables:

Represent

1 2

3

Demand Conservation


31

Ejiuf ij

Kk Pp

k

pkij

},{,}{

}{

1

2

3

4

5

8.0 4.0

8.0 1

2

3

4

5 k = 1 k = 2

6.0

6.0

1

2

3

4

5

14.0

14.0

14.0

4.0 14.0

18.0

1

2

3

4

5

=16

u{2,4}=2

Capacity Contraints


32

Eji

ijij uc

},{

}{}{

Ejiuf ij

Kk Pp

k

pkij

},{,}{

}{

integerand0,realand0 }{ ij

k

p uf

Kkbf k

Pp

k

p

k

,

Minimize:

Subject to:

Single type transmission facility

Determine:

The cost of all transmission facilities to be installed is minimized

The total load on all feasible paths satisfy the demand(s)

The aggregated rate of all flows using edge {i,j} stays below the installed capacity

Encripted Text


33

Eji Tt

t

ij

t

ij uc

},{

}{}{

integerand0,realand0 }{ t

ij

k

p uf

Multiple type transmission facility

Kkbf k

Pp

k

p

k

,

Minimize:

Subject to:

Determine:

The cost of all transmission facilities to be installed is minimized

The total load on all feasible paths satisfy the demand(s)

The aggregated rate of all flows using edge {i,j} stays below the installed capacity

Ejiuf

Tt

t

ijt

Kk Pp

k

pkij

},{,}{

}{

(Still) Encripted Text


34

Optimization Approaches


35

MP is used to describe the minimization or maximization of an objective function of many variables, subject to constraints on the variables

If the objective is a linear function, and the constraints are linear equations and inequalities linear program (LP)

)( xf

mixgi

,,1;0)(

Maximize/Minimize

Subject to :

},...,1{; njSxxj

objective function

constraints

xSx

of spacesolution

Mathematical Programming


36

Many real-life problems belong to NP-complete or even NP-hard problems: probably no fast (polynomial time) algorithm exists for finding an optimal solution

For this kind of problems, exact solutions will not always be possible (due to limitation of computation power, memory-space and time)

One has to settle for a good (but not necessarily optimal) solution Heuristic / Approximation

Approximation


37

Approximation methods :

LP-based solve the LP relaxation, then round-off the solution to the nearest feasible integer

BnB-based terminate, when the temporary best solution is within a certain distance from a lower bound in case of minimization – or an upper-bound in case of maximization

Meta-heuristics general framework that can be applied to many different problems

Specialized heuristics e.g. greedy heuristic : a heuristic that always takes the best immediate (or local) solution while solving a problem iteratively

Approximation (Cnt‘d)


38

Meta-heuristics :

are approximate and non-deterministic (random-triggered) no guarantee for optimal solution within finite time

are high level concepts for exploring search spaces by using different strategies

In the following we will discuss two frameworks that belong to meta-heuristics:

Genetic Algorithm : is inspired by nature‘s capability to evolve individuals influenced by adaptation to the environment

Simulated Annealing : is inspired by the physical process of cooling down a material in a heat bath (a process known as annealing)

Approximation (Cnt‘d)


39

Meta-Heuristics

Meta-Heuristics

Genetic Algorithms, Local Search

Hybridization

Simple Improving Heuristic

Search Algorithm

Solution

Improved Solution

Greedy Heuristic

Search Algorithm

Solution e.g. in terms of a sequence of demands

Objective Value


40

Local Search

A

B

C

D

E

A B

C

D

neighborhood of A

initial solution

move

Plain Local Search (PLS-1)

Search around temporary best solutions

Plain Local Search (PLS-2)

Search around a constant solution

neighborhood of B

neighborhood of C


41

Dimensioning of IP Networks


42

Which routing schemes ?

SPF (eg. IGP)

2

1 2

3 5

2

5

1 2

3 4

5 6

Link Weights

1

2 3

4 5

6

1 2

3 4

5 6

1 2

3 4

5 6

1 2

3 4

5 6

Explicit (eg. MPLS)

Hybrid (eg. DS-MPLS)


43

SPF Routing

Difficult problems

Indirect approach


44

SPF Routing (Cnt‘d)

(b)(a)

6

11

1

1

1

1

2

21

2

3

5

5

121

3 4

5 6

2

3 4

5 6

1

2

4

6

5

3

1

2 3

4 5

1

Driven by link metrics (weights/costs)

Unique shortest path routing vs. Equal-Cost Multi-Path (ECMP)

ECMP e.g. [1-2-4-6] 50% [1-3-4-6] 25% [1-3-5-6] 25%

Unique shortest path routing: 1 unique path for all node pairs


45

DS-MPLS: Class-based Routing

1

1

0

1

1

1

1

1 2

3 4

5 6

}2,1{

4OP

c

;20h

100k

1

2

e t

etty min

Objective Function

Capacity (with OP)

t

tet

d p i

idpdp

OP

edpkyxxc

1

1

e ,

Demand Satisfaction

p

dpu 1 d ,

dpddpuhx

pd ,,

Per-class routing & per-class over-provisioning

Single-path routing

Multi-path routing

realdp

u

binarydp

u


46

Backup Capacity

1 2

3 4 3

2 1

4

normal

backup

Demand (1,4) and (3,4) each

of 20 units

1

3

2

4

40

40

40 20

worst case load on each link

t

tetes

i

idpsidpdpskyzx

))(

1

1

se ,,

p

dpsdp

p

dpsdpsuv )1( sd ,,

ddsdpsdpshvz

spd ,,,

)((dpsdpdps

OP

d p

edpzxc

Demand Rerouting

Capacity

Failure Cases

1

3

2

4

20

20

20 0

normal case load on each link


47

Heuristic Approaches

Two-step strategy:

First consider only normal paths (ALG-1)

Heuristically assign a backup for each normal path


48

Computational Study

Problem (single-path

only)

P1

CPLEX

cost gap(%)

Greedy (best cost of 100 runs)

P2

P3

165.5 | 268.5

166.5 | 268.5

423.5 | 688

6.18 | 9.93

4.19 | 9.47

3.48 | 3.75

190.5 | 310.5

188.0 | 303.5

453.5 | 755

The best result from CPLEX is up to 15% (16%) better than the result from the heuristic

But, the heuristic (two-step strategy) is faster minutes vs. hours


49

Literature


50

(1) Eueung Mulyana, „Efficient Planning and Offline Routing Approaches for IP Networks“, Cuvillier Verlag, Germany, March 2006, ISBN 3-86537-798-X.

(2) Amaro F. de Sousa, „Multi-Layer Traffic Engineering: Network Design based on Integer Linear Programming (ILP)“, COST 279 Second European Summer School, Darmstadt, Germany, September 2003.

(3) EvoNet Evolutionary Computation Resources, „Introduction to Evolutionary Computation“, „How to Build an Evolutionary Algorithm“, EvoNet Flying Circus Slides, available at http://evonet.lri.fr.

(4) Eueung Mulyana, Ulrich Killat, "An Offline Hybrid IGP/MPLS Traffic Engineering Approach under LSP constraints", Proceedings of the 1st International Network Optimization Conference INOC 2003, Evry/Paris France, October 2003.

http://evonet.lri.fr/


51

(5) Dirk Staehle, Stefan Koehler, Ute Kohlhaas. „Optimization of IP Routing by Link Cost Specification“. Technical Report, University of Wuerzburg, 2000.

(6) Dirk Beckmann, Jörn Thurow, „Global Optimization of SDH Networks: a Practical Application“, International Journal of Network Management, 13: 61-67, 2003.

(7) Eueung Mulyana, „MMMCN Part 2 Handout“, available at http://www.tu-harburg.de/et6/staff/mulyana.html.

(8)

http://www.tu-harburg.de/et6/staff/mulyana.htmld




52

Thank You !

Date post:	15-Apr-2017
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Dimensioning of IP Backbone

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