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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
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Convergence of Optical Internet
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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
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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.
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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
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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
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WDM Revolution: Transmission Capability
120 kmOA
120 km 120 km
Optical Amplifiers and WDM - 20 Gb/s
OC-48OC-48
OC-48OC-48OC-48
OC-48OC-48
OC-48
OC-48OC-48
OC-48OC-48OC-48
OC-48OC-48
OC-48OC32/12
OC3/12
OC32
Conventional Transmission - 20 Gb/s@ 1.7 Gb/s
1310RPTR
1310RPTR
1310RPTR
1310RPTR
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LTELTE
40km 40km 40km 40km 40km 40km 40km 40km 40km
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LTE
In Each Direction:12 fibers 1 fiber; 36 regenerators 1 optical amplifier
OA
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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
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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
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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.)
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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
D\S
a
b c
a
0 45 60
b
45 0 0
c 15 0 0
1
0
342
a
c
b
∑∑∑∑
=
s d
sds d
sdsd
R
t
ht
dNHA*
*
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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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tt
ffs
s
i
sji
j
sij ,∀
≠=
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ij
s
ijsij
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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.
)( minmax)(
ljlplSj
DMp
−∈∈ π
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
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