Wavelength Assignment in Optical IP Network

Jin Seek Choi

Information and Communications University

2

Outline

• Introduction– Background– Why Routing and Wavelength Assignment (RWA)?

• Classification of RWA algorithms– Classification: Optimum vs Heuristic

• Classification of WA algorithms– Static WA– Dynamic WA

• Conclusion

3

Background

Internet802 LAN/MANATM/SONETPSTN

Optical Internet-Optical LAN/MAN-Optical WAN

Optical networkusing WDM

technology

Internet is universal connectivity as the standard “glue”.Almost all types of traffic will run over Internet.

Robust, and capable of managing a wide range of failure

Dramatic channel capacity up to Tera bps(vast bandwidth, low attenuation)

Compatibility with SONET, ATM, IPWorldwide deployment in optical fiber

New level of network flexibility

IP/ATM IP/SONETIP/WDM

4

Convergence of Optical Internet

5

Why Optical Internet (IP Network)?

– IP/ATM/SONET/DWDM• slow to scale• multi-layer stack: functional overlap

– Multiplexing: DWDM Σλ=ΣSTM=ΣVC=Σflows=Σpacket– Routing: DWDM, SONET, ATM, IP– Restoration: DWDM=>SONET=>ATM=>IP

– IP/DWDM (Optical Internet)• remove functional overlap -> two layer solution

– IP=>Ubiquitous & Flexibility– DWDM=> Cheap Bandwidth– coordinated restoration at optical/IP level– coordinated (dynamic) path determination at optical/IP level

6

What is Optical Internet?

• Def [OIF]– A data-optimized network infrastructure in which switches

and routers have integrated optical interfaces and are directly connected by fiber or optical network elements, such as dense wavelength-division multiplexers

• Goal– Optical Internet enables a very high capacity Internet.

• IP provides universal connectivity.• WDM switch acts as the main switching/routing device.

7

A New Networking Paradigm

SONET

λ Protection

DWDM

ADM DWDMMux/Demux

OADM

OXC

λ Switching

• Traditional Provisioning– IP network using MPLS-TE– Optical circuits controlled by

TMN – no co-ordination between IP and

Optical domain

• Intelligent Optical Networking– Evolution of transmission networks in a

way that is beneficial to the creation and provisioning of services

– Automatically controlled transport networks– Distributed connection control model– New role for transport management

OBS/OPS

8

What is WDM (Wavelength Division Multiplexing)?

Many to one mapping 16 x OC48 (STM-16) => OC768 lambda4 lambda. Not protocol transparentFiber

OC-48 OC-768

4 λ

9

WDM Revolution: Transmission Capability

120 kmOA

120 km 120 km

Optical Amplifiers and WDM - 20 Gb/s

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OC-48OC-48

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OC3/12

OC32

Conventional Transmission - 20 Gb/s@ 1.7 Gb/s

1310RPTR

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1310RPTR

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LTELTE

40km 40km 40km 40km 40km 40km 40km 40km 40km

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In Each Direction:12 fibers 1 fiber; 36 regenerators 1 optical amplifier

OA

10

Technology core -WDM

SONET

λ Protection

DWDM

ADM DWDMMux/Demux

OADM

OXC

λ Switching

OBS/OPS

Fiber

4 λs

IPdata ctrl

IPdata ctrl

IPdata ctrl

Fiber

11

Say No to opto-electronic networks!

• Optical-electronic conversions and vice versa• Electronic Switching (TDM) currently at 2.5 to 10

Gbps– Will grow to few tens of Gbps

• WDM can provide much higher bandwidth– State of the art in 2000 was 400 Gbps– 1 Tbps in the near future

12

The Need For Speed

• Wavelength Division Multiplexing(WDM)– Is a technique in which multiple channels are operated in

the same fiber simultaneously.Many wavelengths as carriers over the same fiber simultaneously

• Lightpath– Is an optical path established between two nodes in a

network, created by maintaining the same wavelength throughout the path. All optical communication channel between two nodes may span more than one fiber link

13

Technology core -OADM

SONET

λ Protection

DWDM

ADM DWDMMux/Demux

OADM

OXC

λ Switching

OBS/OPS

OADM

IPdata ctrl

OADM

IPdata ctrl

OADM

IPdata ctrl

14

Technology core -OXC

SONET

λ Protection

DWDM

ADM DWDMMux/Demux

OADM

OXC

λ Switching

OBS/OPS

OXC

IPdata ctrl

OXC

IPdata ctrl

OXC

IPdata ctrl

15

Limitations

• Optical devices not very well developed– Optical switches and memories are complex, bulky and in the

experimental stages

• Finite number of available wavelengths– State of the art in Year 2001 supports 80-120 wavelengths

• Degree of wavelength Conversion– No wavelength conversion– Fixed conversion– Full conversion– Limited conversion

• Wavelength continuity constraint leads to poor blocking performance

16

RWA Problem

• The main objective for RWA is to significantly reduce– the network resources required – the overall blocking probability– the maximum of blocking probabilities experienced at all source nodes.

• Two lightpaths must not be assigned the same wavelength on a given link

• If no wavelength conversion is available, a lightpath must be assigned the same wavelength on all the links in its route– Impractical and not scalable– NP-complete is not a good thing

17

Example I

• How many Wavelengths are needed?– The simplest one has 9 WLs ?– One for each lightpath.– Each hop = WL8– P0=WL0– ….– P7=WL7

– Other ?

0

3

14

2p0

p1

p2

p3

p4 p5

p6

p7

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Example I-Greedy

• How many Wavelengths are needed?

– Greedy: 5 WLs?• P1->p2->…->p0

– Other ?0

3

14

2p0

p1

p2

p3

p4 p5

p6

p7

19

Example I-Greedy II

• How many Wavelengths are needed?

– Maximum Sum : 4 WLs?– Other ?0

3

14

2p0

p1

p2

p3

p4 p5

p6

p7

20

What is Routing and Wavelength Assignment?

• Definition Given a set of connections, the problem of setting up lightpaths by routing and assigning a wavelength to each connection.

• Combinatorial problem of routing and wavelength assignment

1

0

6

4

2

35

S0

D0

Route 2

Route 1

Route 3

S1

D2

Route 2

Route 2

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Routing problem

1

0

6

4

2

35

S0

D0

Route 2

Route 1

Route 3

Def= Find available routes between source and destination& Select one of them (lightpath), which is the best one

S1

D2

Route 2

Route 2

22

Wavelength assignment problem

1

0

6

4

2

35

S0

D0

Route 2

Available colors

Def= Find available wavelengths for the given routes& Select one of them, which is the best one for the lightpath

without conflict

D2

S1

23

Constraints and Objectives

• Constraints– Wavelength constraint (wavelength converter)– Time constraint (static vs. dynamic)– Fiber constraint (single vs multiple fibers)

• Typical Criteria– Minimize the network resource requirement– Minimize the connection blocking probability– Maximize the number of connections

24

Types of Routing and Wavelength Assignment

Wavelength constraint

Tim

e co

nstra

int

WL conversionNo conversion

Stat

icD

ynam

ic

Fiber=2Fiber=x

Fiber=1

Static routingWith limited

WL conversion

Who consider?

staticRouting & WA

DynamicRouting & WA

offline circuitSwitched routing

Circuit switchedrouting

full limited

Location

sparseeverywhere

25

Time Constraints

• Types of connection requests• Static

– All connection requests are known in advance . Do not change with time.

• Incremental– Connection requests arrives sequentially and are never

taken down once established

• Dynamic– Similar to incremental, but connections are taken down

after a finite amount of time.

26

Wavelength Continuity Constraints

• Wavelength Converters– Full Range Wavelength Converters (FWC)

:converts an incoming wavelength to any out going wavelength

– Limited Range Wavelength Converters(LWC):converts an incoming wavelength to a subset of outgoing wavelength

• Sparse Wavelength Converters– All Nodes have wavelength converter.– A partial set of nodes have wavelength converter.

27

Solutions?

• Formulations proposed for representing the RWA problem– For a determined set of lightpaths, a route the lightpaths

and assign wavelengths to them.– Maximum number of lightpaths should be established

(while minimizing blocking).– Formulation with Integer Linear Programming.– Computing intensive. Good for small networks only

• Heuristic algorithms proposed– Problem size reduction through pruning.– Randomized rounding: fractional flow vs. integer flow– Sequential coloring algorithm

28

Classification of RWA

RWA algorithm (NP-complete)

Optimal Algorithm(Near optimal)

Heuristic Algorithm

Approach mechanismsLinear optimize problemmulti-commodity flow problem…

Heuristic Programming- Greedy, - TABU, - Genetic,- simulated annealing ...

Size reductioncase reduction

Routing and Wavelength Assignment

Optimal AlgorithmHeuristic Algorithm

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Optimal Algorithm

• Objective– Minimize the number of wavelengths needed to establish a

given number of connections. Or,– Maximize the number of connections for a given set of

wavelengths ( tends to setup shorter connections ), Or– minimize the number of lightpaths passing through any

link. etc.

• Conceptually, mathematical optimization!– Mostly in the family of Integer Linear Programming (NP-

complete; thus, good heuristics, simplifying network topology are focused.)

31

Maximum Network Congestion

A = lower bound on total traffic, HR = average hop count with routing scheme R, N = # of nodes, d = degree

dNHA R

**

How do you measure maximum network congestion or network load?

32

Mixed Integer Linear Programming Formulation

• Given the traffic matrix Λ, where Λsd=number of connections needed between source s and d,

- Traffic Matrix Λ

- Maximum Network Congestion

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t

ht

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*

33

MILP Variables

– ts = denotes the total traffic originating from node s

• hsd = # of hops from node s to node d• h = average # of hops in the network

• fij = traffic/congestion on lightpath originating from node i to node j

• fmax = maximum congestion in network = maxij fij• δi

I = logical in degree of node i• δi

O = logical out degree of node i• biJ ε { 0, 1} = binary variables to indicate whether there is a

lightpath from i to j

∑d sdt

34

MILP Constraints

• Flow of conservation at each node

• Total flow on lightpaths

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• Degree Constraints

• Variable range constraints

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35

Algorithms to solve Static problem

• Static : – All computations done offline

• MILP (Mixed Integer Linear Programming)– Large Traffic Matrices leads to less fun– Extremely time consuming – NP compelete– Shown to be numerically intractable even for networks

with a moderate number of nodes

• CPLEX Problem Solver and given sub-traffic matrices due to the given traffic matrix– Shown to be numerically intractable even for networks

with a moderate number of nodes

36

Algorithms to solve Dynamic problem

• Dynamic:– For each new request of a light path, determine the path

and the wavelength(s)– Connection request arrival model is a random process (e.g.

Poisson)– Connection durations are random variables.

• The objective is to minimize blocking probability. • Solution:

– In my view, it is stochastic dynamic programming problems with enormous complexity.

37

Moral of the story so far

• Both static and dynamic lightpath establishment problems are difficult if we try to find an exactly optimal solution.

• General heuristics on integer linear programming– This problem has good heuristics; namely

“randomized rounding”.– Nonintegral multicommodity flow– Path stripping

38

Going further for Heuristics Approaches

• Decompose routing and wavelength assignmentsubproblems for the case of w/o wavelength converters.– Wavelength continuity constraints

• Determine routes for all the requested connections; (circuit switch routing)

• Assign wavelengths to all – that no same wavelenghts are used for the paths sharing an

identical link.– The objective is to minimize the number of wavelengths

used

39

Routing Approaches

• Fixed routing– Route is pre-computed using some algorithm (e.g., Shortest Path First).– A given connection always takes the same route

• Fixed alternate routing– Each node maintains a table of alternate routes (pre-computed) to

different destinations

• Adaptive routing– Each node has network state information

• state:set of connections currently in place– Route chosen dynamically depending on network state– Lower blocking probability than fixed and fixed alternative routing– Requires expensive support from control and management protocols to

continuously update routing information at the nodes

40

Wavelength assignment Strategies

• Static WA:– Given a set of lightpaths and their routes, assign wavelengths to light

paths such that no two lightpaths share the same wavelength on a given fiber link.

– Can be formulated as graph coloring problem.• Dynamic WA:

– Context • A connection request arrives• Dynamic routing decides a path• Dynamic wavelength assignment decides the wavelength

– Objective• Minimize blocking probability

– Solutions:• Random, First Fit, Least used/SPREAD, Most used/PACK, Min Product,

etc,…

41

Functional Classification of RWA

RWA algorithm

Routing problem Wavelength assignment problem

Search(method+order)

Selection(order+rule)

Search Selection(order+rule)

Sequential Combinatorial

Greedy - Heuristic- Optimal

Sequential Combinatorial

Greedy - Heuristic- Optimal

Wavelength Assignment

Static AlgorithmDynamic Algorithm

43

Static Wavelength Assignment

• Static– All connection requests are known in advance . Do not

change with time.

• Objective– Maximize average number of connections established, Or– Minimize the number of lightpaths passing through any

link. etc.

• Problem– Good heuristics– Equivalent to the Restoration Algorithm

44

Relation to Graph Coloring

• Converting a graph G into a path graph P(G)• Problem is shown to be NP-Complete• Hence, only specific topologies will have a solution, others

will an approximate solution– Greedy

45

Dynamic Wavelength Assignment

• Dynamic– For each new request of a lightpath, determine the

wavelength

• Objective– Minimize blocking probability– Maximize average number of connections established

• Problem– Good heuristics– Equivalent to the Restoration Algorithm

46

Graphical Formulation

1

0

6

4

2

35

S1

D1

Route 2

Route 1

Route 4 Route 3

• Input– Physical topology– A connection request arrives (poisson arrival or others)

25%35%25%10%50%

usage12345

47

Solutions

• Random (R)– Available wavelengths for the required route are determined– A wavelength is selected randomly

• First Fit (FF)– All wavelengths are numbered– Lower numbered wavelength is selected first (1,2,3,..)

• Least Used (LU)– Selects the wavelength that is least used Tends to breakup long

wavelength paths – Performance worse than Random– Not preferred in practice

• Most Used (MU)– Selects the most used wavelength– Slightly out performs FF

48

Solutions II

• Min Product (MP)– Minimizes number of fibers in network– Computes for each wavelength ‘j’– Chooses wavelength such that above value is minimized– Does not perform as well as multi-fiber version of FF

• Least Loaded– Selects the wavelength that has the largest residual capacity

on the most loaded link along route ‘p’

– LL outperforms MU and FF in terms of blocking probability in a multi-fiber network.

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49

Functional classification of WA

Heuristic WL assignment-Random-First fit-Least used-Most used-Min product-Least loaded-MAX SUM-Protection threshod...

Graph Coloring- Greedy

Static problem

Wavelength assignment problem

Dynamic problem

50

Conclusions

• We summarize some Technical issues for RWA.– Why RWA– RWA problem classification– WA problem classification