Download Design of Traffic Grooming Optical Virtual Private Networks Obeying

Design of Traffic Grooming Optical Virtual Private Networks Obeying
Physical Limitations
Ákos Szödényi, Szilárd Zsigmond, Balázs Megyer, Tibor Cinkler
High Speed Networks Laboratory, Department of Telecommunications and Media Informatics,
Budapest University of Technology and Economics
2nd Magyar Tudósok Street, Budapest, ZIP-1117, Hungary, Europe
Email: {szodenyi, zsigmond, megyer, cinkler}@tmit.bme.hu
Abstract— Physical impairments in both Metropolitan Optical
Networks (MON) and Long Haul Optical Networks (LHON) often
influence the signal quality, therefore a proper routing decision is
needed. In this paper we investigate how to design traffic
grooming optical Virtual Private Networks (oVPN), over WDM
(CWDM or DWDM) networks, while obeying the properties of the
physical layer. Although we obtain these limiting properties using
simulations, actual measurements can be used as well.
Optimization was achieved by Integer Linear Programming
(ILP)[1], where the physical limitations are represented using a set
of new variables and constraints. Since the problem is NP hard,
we decompose it using heuristics into multiple, less complex, subproblems. The proposed method is illustrated in a case study.
Index Terms— BER, 3R, physical constraint, oVPN, ILP
I. INTRODUCTION
Routing and wavelength assignment (RWA) takes a central
role in the control and management of an optical network.
Many excellent papers have been written about constraint
based routing which obeys physical effects. See e.g. [2-6].
Polarization mode dispersion (PMD) was considered in [2,3].
Amplified spontaneous emission (ASE) and crosstalk (XT)
were investigated in [4]. A new model for dynamic resource
allocation was presented in [5]. The impact of the optical
switches and amplifiers was also included in [6]. A new
constraint-based routing, which includes the PMD and optical
signal-to-noise ratio (OSNR), is presented in [7]. In a very
recent study the performance engineering of transparent
metropolitan area optical networks was presented in [8].
The widely accepted approach is to simplify the physical
layer effects and split them into two categories: linear effects
(PMD,ASE, etc.) and nonlinear effects (XT, etc.). The linear
effects can be taken into consideration in the same way as the
recently mentioned papers did. Modeling nonlinear effects is
Tibor Cinkler has been supported by the OTKA Postdoctoral Research
Grant D42211 and by the János Bolyai Postdoctoral Research Fund. Akos
Szodenyi has been supported by the Leonardo grant while doing this work at
CTTC in Barcelona, Spain.
very difficult and is usually approximated using a linear
approach similar to [9]. The main idea of our model is to take
every effect into consideration by characterizing the optical
signal using Bit Error Rate (BER). For the optical layer there
is no commercially available signal regeneration, therefore the
noise and signal distortion accumulate along the light path. The
performance of the transmission system is often characterized
by BER, which must be smaller than [approximately] 10-9 at
the start of most installed systems. To calculate the BER which
characterize physical effects is much simple and more efficient
than taking all the linear and nonlinear effects into account.
This article is organized as follows: first we investigate the
physical layer, then we describe the optimization process based
on ILP and finally, we present the conclusions of our work.
II. PHYSICAL IMPAIRMENTS
Physical effects will be discussed in this section. First we
explain why it is necessary to focus on physical impairments.
Second is a case study that shows how these effects are taken
into account and finally the simulation result will be presented
for the case study.
Due to the dramatic growth of internet traffic, service
providers and telecom operators are forced to adopt higher and
higher speeds in transport networks. In the past, only point to
point optical networks were used, but recently several ring
networks have been adopted, which use wavelength division
multiplexing (WDM) technology to enlarge the transmission
capacity. Optical layers require an increasing amount of
functionality to support the growing high-speed transport
networks. Thus, optically transparent networks are under
investigation and seem to be a good candidate for future optical
networks. In these networks the light is detected only at the
receiving termination point. On one hand, eliminating O/E/O
conversion is necessary since the optical/electrical conversion
is a “bottleneck”. On the other hand, replacing O/E/O
conversion with 3R optical regeneration has some drawbacks;
it has design imperfections and has to be economically
optimized. For larger transparent optical networks it makes
sense to focus on physical limitations because a signal can
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using Q = | µ1 − µ0 | , where µ1 is the mean voltage level of
σ1 + σ 0
the logical level ”1” and µ0 is of the logical level ”0”. σ0 and σ1
are the standard deviation values for the noise distributions on
the 0 and 1 logical voltage levels, respectively. By varying the
decision threshold of the receiver diode, the sensitivity of the
system can be changed. The Q factor is obtained by evaluating
this sensitivity change. This is the “Variable Threshold
Method” which is discussed in more detail in ITU-T G.976
(1997).
Fig. 1 shows the BER estimated values when the signals pass
through a given number of optical nodes (we consider a limit of
10 nodes). Fig. 1 compares the transparently crossed optical
nodes to the BER estimated value for the previously mentioned
OADM filter technology.
-4
-5
-6
-7
Log (BER)
become impaired and useless when routed over long distances
and through several nodes. This must be avoided and therefore
different constraint based routing algorithms have been
invented. One solution for this problem is O/E/O conversion.
This necessary conversion opens other new possibilities, such
as traffic grooming, which is an efficient way to make the
network cost-effective. Our main idea is to place grooming
capable nodes, not only to effectively groom traffic demands,
but also to avoid too many signal impairments. This is why our
work was focused on physical limitations.
In the following the case study will be described to explain
how the investigation of the physical limitations has been done.
We also would like to point out, that VPI simulation can be
done for every possible optical network. The case study
compares three different technologies that are implemented for
one OADM goal.
In our simulations [10] it is assumed that an optical signal
(modulated at 2.5Gbit/s) is traveling along several optical
nodes in a metro optical network using one of the three
different filtering technologies (Mux/Demux, Arrayed
Waveguide Grating (AWG) or Fiber Bragg Grating (FBG)
optical add drop multiplexers). It has an EDFA pre-amplifier at
each node and a fiber length of 35 km between adjacent nodes.
All data for components introduced in the simulation is in
accordance with worst-case experimental data found on the
datasheets of optical components. Optical signals, which are
not dropped at a given node, experience a double attenuation
because they are first de-multiplexed and then re-multiplexed at
the pass-through nodes.
The VPI program estimates the BER using the following
equation:
+∞
2
2
1
⎛ Q ⎞ where
erfc ( x ) =
e −t dt
BER = erfc⎜
⎟
∫
2
π
2
⎝
⎠
x
where Q is the quality factor [11]. The ”Q” factor is a measure
of the digital signal eye aperture. It adopts the concept of S/N
ratio in a digital signal and is an evaluation method that
assumes a normal noise distribution [12]. It can be calculated
-8
-9
-10
-11
-12
MUX node
AWG node
FBG node
-13
-14
-15
4
5
6
7
8
9
10
Number of nodes passed through
Fig. 1. Number of crossed optical nodes vs. BER foreseen (a. Mux/Demux
based, b. AWG based, c. FBG based)
If BER were fixed to 10-9, the situation would be quite
different for the three node solution. The highest hop number
that could be passed transparently for Mux/Demux and AWG
is only four and five, respectively. If a grooming-capable node
is not reached and FBG is employed, then ten optical nodes
could be passed without requiring signal regeneration.
The main reason for taking the physical limits into
consideration is to set a limit for the virtually existing optical
links by determining the maximum BER degradation to 10-9. If
the BER value of a light path is higher than the limit, the
optimization process will exclude this light path. This will be
explained in the next section.
III. ILP FORMULATION OF THE OVPN CONFIGURATION
PROBLEM
We used a special network model in our work to represent
both the electrical and the optical layer in one graph. We will
refer to it as the Wavelength Graph (WG)[13]. For each
physical link we assigned as many WG edges as the number of
assigned wavelengths in our network. The nodes of the
physical network are represented by sub-graphs and the
electrical layer is represented by an “Electrical Vertex”. The
edges of wavelength graph are weighted to determine which
layer is preferred ( electrical or the optical).
We formulated the problem as an integer linear program
(ILP), which led to an NP hard problem. To decrease the runtime of the optimization process, we had to decompose the
original problem into multiple small problems. The division of
the problem means that we route only one demand at a time.
The ILP formulation handles flow similarly to the ILP
formulation of the Minimum Cost Multi-Commodity Flow
(MCMCF) problem [13], but it deals with traffic grooming and
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physical constraints as well.
Let D(V,E,C) be an undirected graph where V is the set of
vertices, E is the set of edges and C = cl , clphy ∈ R + , l ∈ E is
{
}
the cost of using edge l. cphy denotes the physical cost of the
given link, which includes the length of the link. In the case of
an inner link, in a node-modeling sub-graph , this is an
equivalent cost representing the signal degradation at the
specified node.
Set O = o s o , t o , b o : s o , t o ∈ V , b o ∈ R + is the set of all
demands defined by its source so, sink to and bandwidth
requirement bo. The set O, of all demands o, is the union of
subsets Op corresponding to the different oVPNs p ∈ P , i.e.,
p
O p = { } and Υ O = O .
{(
Ι
∀p∈P
)
}
∀p∈P
Note: between a pair of nodes there can be multiple different
demands, however, in that case they are assumed to belong to
different oVPNs.
VE ⊂ V is the set of vertices representing the electrical
vertex, capable of adding or dropping traffic as well as
performing traffic grooming, i.e. time division multiplexing.
We denote E E ⊂ E as the edges connected to an “electrical
vertex”.
Let Ei denote the set of edges connected to vertex i. The
number of the wavelength channels is assumed to be equal for
all fibers, and each link consists of two fibers carrying
information in opposite directions.
Bl. is the capacity of the wavelength channels. For simplicity,
the capacities of the wavelength channels are assumed to be
equal.
When a demand is routed through multiple wavelength-paths
we call it multi-hop route. This route is segmented by electrical
vertices; traffic grooming is performed. The route between two
neighboring electrical vertices is called a hop.
A physical limitation must be applied to each hop and that is
why we must define the following variables for each hop
separately. We represent the set of hops by H.
Variable x(l ,h ) ∈ (0,1) denotes the flow of the current
commodity in link l ∈ L , assigned to hop h ∈ H .
Variable y(i , h ) ∈ (0,1) denotes the direction of the specified
hop through nodes i ∈ V .
Variable z(i ) ∈ (0,1) denotes the nodes in the direction of the
demand through the nodes i ∈ V .
Here we implicitly assume that one specified wavelength
path might be used by traffic streams of oVPN only one at a
time.
The objective is to minimize the cost function below:
COST = min (α ⋅ C E + (1 − α ) ⋅ CO )
(1)
where
(2)
(0 ≤ α ≤ 1)
∑ ∑c b
CE =
o
l
l∈AE ∀h∈H
∑ ∑c c
CO =
l∉ AE ∀h∈H
l
xl ,h
phy
l
xl ,h
- electrical cost
(3)
- optical cost
(4)
Subject to constraints:
∑x
l ,h
b o ≤ Bl
∀l ∈ E
(5)
∑x
l ,h
= 2 yi , h
∀i ∉ VE ; ∀h ∈ H
(6)
= y i ,h
∀i ∈ V E ; ∀h ∈ H
(7)
h∈H
l∈Ei
∑x
l ∈E i
l ,h
{
∑x
= 2 zi
∀i ∈ VE \ s o , t o
∑x
=1
∀i ∈ s o , t o
h∈H ,l∈Ei
h∈H ,l∈Ei
l ,h
l ,h
{
⎛
∑ max⎜⎝Used , ∑ x
l
l ∈E i
∑x
l ,h
∑y
i,h
l∈ E
h∈H
h∈H
l ,h
xl ,h = 0
(9)
{
∀h ∈ H
∀i ∈ V E
(8)
}
⎞
o o
⎟ = 2 zi ∀i ∈V \ s , t
⎠
c lphy ≤ PhyLimit
≤2
}
}
(10)
(11)
(12)
∀l ∈ {ForbiddenLinks}, ∀h ∈ H (13)
Variables:
xl , h ∈ {0,1}
y i , h ∈ {0,1}
zi ∈ {0,1}
∀l ∈ E , ∀h ∈ H
(14)
∀i ∈ V , ∀h ∈ H
(15)
∀i ∈ V
(16)
Our goal is to minimize the network resources used by the
routed demand. We prefer to route shorter demands with larger
bandwidth first. The program routes the first link and then
marks it and decreases its available capacity to prevent other
VPNs from using it.
We can utilize our resources better if we use traffic
grooming. We can achieve this by forcing the demand to use
the links that were previously used by another demand of the
same VPN. By the reuse of these links their costs will be
decreased for the considered VPN. Note that demands of
different VPNs cannot be multiplexed.
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IV. RESULTS
As mentioned before, we could set up new restrictions based
on the optical layer constraints, which can easily be taken into
account in the routing optimization process. We used a traffic
grooming optical VPN optimization process to demonstrate the
impact of physical constraints. The test networks have 6 and 9
nodes respectively. The use of larger networks was limited by
the long run-time of the optimization process. The effects of
the physical impairments were demonstrated by comparing two
simulation methods.
The first one was done without
considering the physical limits and the second one included
them. The optimization process was performed by a cost
function based on the parameter α. We inspected the optical
and electrical cost dependency from parameter α, changed
parameter α and compared the two methods. Fig. 2,3,4 and 5
show the differences between the two optimization processes.
We can see that the differences disappear where the use of
optics is very cheap (large α) and the differences are huge
when the parameter α is near zero. To understand the results
we have to investigate the optimization processes where α is
near zero. If α is very low, the optimal route will be longer
than with higher α values because of the negligible cost of
optical-electrical-optical conversions. For longer routes the
physical effects will have more impact on the optimization
process.
with physical limitations
without physical limitations
Electrical cost
5000
4500
4000
3500
3000
2500
0.0
0.2
0.4
0.6
0.8
1.0
Alpha parameter
Fig.2. Electrical cost dependency on
α parameter Test Network1
with physical limitations
without physical limitations
2400
2200
Elecrtical cost
Our objective (1) can also be fine-tuned by the parameter α.
If α = 0, then we want to minimize the total number of
wavelength-links used by the demand. If α = 1, then we want
to minimize the usage of electrical resources. In this case the
method will prefer optical links.
Constraint 5 states that the amount of traffic using an edge
may not exceed the capacity Bl of that light-link.
Constraint 6 guarantees that the traffic in each hop is
continuous in the optical domain.
Constraint 7 guarantees that each hop starts or ends in an
electrical vertex.
Constraints 8 and 9 ensure that the route of the demand starts
at the source vertex and it ends at the target vertex and it is
continuous at the electrical nodes.
In connection with the previous two, constraint 12
guarantees that a maximum of 2 hops will lead to an electrical
vertex, and fulfill the continuity condition.
Constraint 10 means that the optical routes cannot branch or
cross each other. Usedl denotes whether the specified link is
used by the current VPN or not.
Constraint 11 is the physical limitation. It states that along
any hop the cumulated BER degradation (physical cost) cannot
reach the measured maximum value, which is represented by
PhyLimit. Here we are able to influence the physical design,
because unless constraint 11 is fulfilled, either that link will be
routed on another way, or grooming capable node will be
installed during that link.
Constraint 13 simply means that if a link is forbidden, i.e. it
has already been assigned to another VPN, or the available
capacity is not enough for the current demand, then it can not
be used by any of the hops.
2000
1800
1600
1400
1200
0.0
0.2
0.4
0.6
0.8
1.0
Alpha parameter
Fig.3. Electrical cost dependency on
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α parameter Test Network2
VI. REFERENCES
with physical limitations
without physical limitations
2600
[1] T. Cinkler: "ILP Formulation of Grooming over Wavelength Routing with
Protection", IFIP ONDM 2001, 5th Conference on Optical Network Design and
Modeling, Wiena, February 2001
2400
Optical cost
2200
[2] M. Ali, D. Elie-Dit-Cosaque, and L. Tancevski, “Enhancements to MultiProtocol Lambda Switching (MPλS) to Accommodate Transmission
Impairments,” GLOBECOM ’01, vol. 1, 2001, pp. 70–75.
2000
1800
1600
1400
[3] M. Ali, D. Elie-Dit-Cosaque and L. Tancevski “Network Optimization with
Transmission Impairments-Based Routing” European Conf. Opt.
Commun.2001, Mo.L.2.4, Amsterdam, The Netherlands, 2001, pp. 42–43.
1200
1000
800
600
0.0
0.2
0.4
0.6
0.8
1.0
Alpha parameter
Fig.4. Optical cost dependency on
α parameter Test Network1
[5] Y. Tang et al., “Investigation of Dynamic Resource Allocation based on
Transmission Performance Analysis and Service Classification in WavelengthDivision-Multiplexing Optical Networks,” Optics Commun., Nov. 2002, vol.
212, pp. 279–85.
2600
2400
Optical cost
2200
2000
1800
1600
[6] J. F. Martins-Filho et al., “Novel Routing Algorithm for Transparent Optical
Networks Based on Noise Figure and Amplifier Saturation,” IMOC 2003, Proc.
2003 SBMO/IEEE MTT-S Int’l., vol. 2, Sept. 2003 pp. 919–23.
with physical limitations
without physical limitations
1400
[7] Yurong (Grace) Huang, Wushao Wen, Jonathan P. Heritage1 and
Biswanath Mukherjee, “Signal Quality Consideration for Dynamic Connection
Provisioning in All-Optical Wavelength Routed Networks” OptiComm, Oct.
2003.
1200
1000
800
600
0.0
[4] B. Ramamurthy Debasish Datta, Helena Feng, Jonathan P. Heritage,
Biswanath Mukherjee, “Impact of Transmission Impairments on the Teletraffic
Performance of Wavelength-Routed Optical Networks,” IEEE/OSA J.
Lightwave Tech., vol. 17, no. 10, Oct. 1999, pp. 1713–23.
0.2
0.4
0.6
0.8
1.0
Alpha parameter
Fig.5. Optical cost dependency on
α parameter Test Network2
To have a feedback mechanism in the optimization process
we calculated the BER of each route; they were different for
both processes. These calculations were outlined in the chapter
entitled “Physical effects”. We found that the reason for the
differences is the physical layer constrains.
V. CONCLUSION
In this paper we demonstrated a highly complex but very
efficient way of taking into account the physical constrains of a
routing process. Designing oVPNs over WDM networks and
engineering their performance could be a good solution for
improving optimization methods and creating oVPNs
according to their physical environment. Our simulations
showed that the new optimization method affected the number
and the position of the optical-electrical-optical conversions
and the optimal grooming locations.
[8] Ioannis Tomkos et al., “Performance Engineering of Metropolitan Area
Optical Networks through Impairment Constraint Routing” OptiComm, August
2004
[9] John Strand Angela L. Chiu and Robert Tkach “Issues For Routing In The
Optical Layer” Communication Magazine, February 2001
[10] VPI photonicsTM www.vpisystems.com
[11] ANRITSU CORPORATION, Application Notes: Q Factor
Measurement/Eye Diagram Measurement, SDH/SONET Pattern Editing
http://www.eu.anritsu.com/files/MP1632_1763_1764_EF1100.pdf
[12] TIA/EIA-526-9: OFSTP-9: Accelerated Measurement Procedure for
Determining BER and Q-factor in Optical Transmission Systems Using the
Variable Threshold Method http://www.tiaonline.org/standards/
[13] B. Megyer, Z. Szombat, T. Cinkler: "Methods for Setting up VPλNs with
Traffic Grooming and Protection" IFIP ONDM 2003, 6th Conference on
Optical Network Design and Modeling, Budapest, February 2003
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