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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
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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
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The Internet & Information Transfer
TESRG Dimensioning of IP Backbone Eueung Mulyana
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The Internet
Global Crossing
UUNet
AI7 IM7
NJIT
MIT
Cimahi-Net
Telekomnet GarutNet
AS
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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
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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
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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
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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
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Routing in IP Networks
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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
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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
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Dest. Next hop Interface
1 3 4-3
3 direct 4-3
5 3 4-3
2 direct 4-2
6 direct 4-6
Dest. Next hop Interface
1 direct 3-1
4 direct 3-4
5 direct 3-5
2 1 3-1
6 4 3-4
Dest. Next hop Interface
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
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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
Dest. Next hop Interface
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
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Intra-Domain Routing: Shortest Path Routing (ECMP) – (2)
1 2
3 4
5 6
6
6
6
6
6
6
6
6
Dest. Next hop Interface
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
Dest. Next hop Interface
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
Dest. Next hop Interface
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
Dest. Next hop Interface
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
Dest. Next hop Interface
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
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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
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Traffic Engineering, Network Dimensioning & Planning
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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
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A new Telco Company
Customers
Partners, Providers, Vendors
Demands
Cost Model
Management Reqs./Cons.
The Need (Cnt‘d)
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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 ?
TESRG Dimensioning of IP Backbone Eueung Mulyana
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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
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Link Dimensioning Number
of channel Integer
Erlang B formulae
Blocking
Offered traffic
Given:
Traffic Matrix
Required:
Number of channels for each link ( )
Solution:
TESRG Dimensioning of IP Backbone Eueung Mulyana
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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
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1
3
2
4
5
1
3
2
4
5
STM1
STM4
IP Networks (Cnt‘d)
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Basics of Network Dimensioning
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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
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Optimization Algorithm
Input (data, parameters,etc.)
Constraints Objective(s)
Output
A System View
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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)
TESRG Dimensioning of IP Backbone Eueung Mulyana
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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
TESRG Dimensioning of IP Backbone Eueung Mulyana
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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
TESRG Dimensioning of IP Backbone Eueung Mulyana
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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
TESRG Dimensioning of IP Backbone Eueung Mulyana
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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
TESRG Dimensioning of IP Backbone Eueung Mulyana
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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
TESRG Dimensioning of IP Backbone Eueung Mulyana
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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
TESRG Dimensioning of IP Backbone Eueung Mulyana
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Optimization Approaches
TESRG Dimensioning of IP Backbone Eueung Mulyana
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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
TESRG Dimensioning of IP Backbone Eueung Mulyana
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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
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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)
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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)
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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
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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
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Dimensioning of IP Networks
TESRG Dimensioning of IP Backbone Eueung Mulyana
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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)
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SPF Routing
Difficult problems
Indirect approach
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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
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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
TESRG Dimensioning of IP Backbone Eueung Mulyana
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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
TESRG Dimensioning of IP Backbone Eueung Mulyana
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Heuristic Approaches
Two-step strategy:
First consider only normal paths (ALG-1)
Heuristically assign a backup for each normal path
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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
TESRG Dimensioning of IP Backbone Eueung Mulyana
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Literature
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(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.
TESRG Dimensioning of IP Backbone Eueung Mulyana
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(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)
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Thank You !