Comparison of Opaque and Translucent WDM
Networks with Different Regenerator-Placement
Strategies under Static and Dynamic Traffic
Giuseppe Rizzelli, Guido Maier, Romolo Longo, Achille Pattavina
Department of Electronics and Information, Politecnico di Milano, Via Ponzio 34-35, 20121 Milan, Italy
Email: {rizzelli,maier,pattavina}@elet.polimi.it
Abstract—In this paper we compare the performance of
opaque and translucent Optical Transport Networks (OTNs)
taking into account different regenerator placement strategies.
The translucent approach achieves noticeable results under static
traffic in terms of saving of deployed resources. On the other
hand in the dynamic scenario a translucent OTN suffers from
fewer resources and may display high blocking probability. We
describe our planning procedure that is composed of two main
steps: the first one selects a subset of network nodes that can host
regeneration devices solving the Regenerator Placement Problem
(RPP) by the means of three different heuristic allocation
algorithms; the second one computes the amount of resources
(number of transponders and DWDM systems) accomplishing the
Routing and Wavelength Assignment with Regenerator Problem
(RWA-RP) to satisfy a given set of static traffic demands. This
dimensioning procedure is applied both to a translucent and to
an opaque implementation of the same OTN showing the CAPEX
benefit of the first approach. Subsequently both translucent
and opaque versions of the OTN are simulated under dynamic
traffic, displaying the greater robustness of the opaque case to
blocking, no matter what regenerator placement algorithm has
been adopted. To back up this conclusion we carried out a further
analysis on the power consumption.
Index Terms—Regenerator Placement Problem (RPP), sparse
placement, Routing and Wavelength Assignment with Regenerator Problem (RWA-RP), translucent Optical Transport Network
(OTN).
I. I NTRODUCTION
The demands for higher bandwidth at lower cost is increasing substantially in today’s communication networks.
The continued growth of broadband services is making the
service providers and equipment vendors looking for scalable
optical networks. One of the major limitations to scalability
is power consumption, which in the case of OTN is very
much related to the number of Optical-Electrical-Optical (OE-O) conversions a connection undergoes. O-E-O interfaces
are commonly needed to carry out both regeneration (3R:
reamplification, reshaping and retiming) and wavelength conversion (while we are still waiting for an all-optical regenerator
to appear on the market). Since O-E-O converters are also
The work described in this paper was carried out with the support of the
BONE-project (”Building the Future Optical Network in Europe”), a Network
of Excellence funded by the European Commission through the 7th ICTFramework Programme.
expensive devices, these issues lead to the development of
optical-bypass technology: a connection remains in the optical
domain from its source to its destination employing nodes such
as directionless/colorless Reconfigurable Optical Add Drop
Multiplexers (ROADM) or Optical Cross Connects (OXC)
[1][2]. As well-known, different kinds of OTNs are currently
identified on the basis of their utilization of (O-E-O) devices:
opaque, transparent and translucent networks [3][4].
An opaque network is characterized by hosting O-E-O interfaces in the nodes at both ends of each link of the network for
every lightpath; this approach simplifies network management,
design and control, as it implies a full independence of the
logical layer from the physical layer. On the other hand, it
requires a huge number of O-E-O devices greatly increasing
the total network cost and power consumption.
At the opposite extreme we have the transparent OTN: in
such a network O-E-O conversions do not occur as all signals
bypass the intermediate nodes without regeneration. With optical transparent switching, OTN design and operation become
cross-layer problems coupling the physical to the logical layer:
transmission impairments and wavelength assignment have
to be taken into account jointly with traffic demand when
planning the network and assigning resources to the lightpaths.
Eliminating O-E-O conversions completely from the network
is possible only for limited-size plants because transmission
impairments limit the maximum distance reachable from the
source node and thus the geographical extension of the network.
For most wide-area networks the only viable option is the
translucent approach in which both opaque and transparent
features co-exist in a node: an O-E-O operation is performed
if either the signal quality falls below a certain threshold or λ
conversion is needed to avoid wavelength blocking [5][6][7].
Translucent network planning aims at employing the smallest
possible number of regeneration resources. Various European
Projects (i.e. DICONET, PHOSPHORUS, NOBEL) dealt with
translucent dimensioning and in many works [10][11][12] a
particular emphasis is given on where to deploy regenerators
to minimize the number of rejected connections in a dynamic
scenario. Other studies are dedicated to the minimization of
the number of transponders and fibers to satisfy a given static
traffic [13]. Thus, current literature shows the advantages of
the translucent approach both under static and dynamic traffic.
Specifically, in the dynamic scenario, smart 3R placement
algorithms seem to make translucent OTN comparable or even
better performing than the opaque case. This conclusion is
based on the assumption that a pre-assigned total number of
transponders is shared by a set of regenerating nodes (i.e.
those nodes which can host regenerators) previously selected
by some algorithms; in other cases, the maximum number of
transponder per regenerating node has been fixed. These assumptions are not very realistic in a green-field dimensioning
phase: the total number of regenerators in the network (or in
each node) should be a result of planning, rather than being
fixed a priori. Moreover, in the cited works no constraints
on the number of DWDM systems are considered, sometimes
setting their number to infinity in order to avoid blocking
due to lack of wavelengths. This tends to compensate the
scarsity of 3R resources by giving more possibilities to route
a connection in different ways.
In this paper we would like to analyze translucent OTNs by
a different and, we believe, more realistic approach. First,
we minimize the number of 3R resources but we do not
constrain it a priori. Second, when comparing the translucent
to the opaque implementation of an OTN we also take into
account how many extra-DWDM systems are required by
“translucency” in the static scenario. Then, we also compare
blocking performance in the dynamic scenario. This global
point-of-view comparison is presented for the first time to the
best of our knowledge.
This work is organized as follows: section II presents the
OTN model and the network design phase; section III shows
the sensitivity of the quantity of extra resources and number
of regenerator nodes compared to the opaque case to the 3R
placement algorithm; section IV describes the performance of
the formerly designed networks when experiencing dynamic
traffic; section V describes a power consumption model and
evaluation under static and dynamic traffic; the conclusions
are drawn in section VI.
II. T RANSLUCENT D ESIGN
The problem of network design for a translucent OTN can
be segmented into two steps as follows: given the topology
of the network in terms of switching nodes and links, first
we choose a subset of network nodes that are provided
with regeneration capability (the so-called S3R set) and then
we perform the quantification of resources (transponders and
DWDM systems) to satisfy a given set of demands for static
optical circuits. Both the steps are carried out by heuristic
algorithms that operate under the constraints imposed by the
physical layer (propagation impairments).
A. Translucent OTN model
The OTN model that we consider is composed by the
following elements: translucent optical nodes and unidirectional DWDM systems. A translucent node consists of an alloptical non-blocking switching fabric together with a certain
number of 3R units (transponders) and is able to switch an
optical signal from an input port to an output port without
electrical processing (introducing attenuation). The core of the
translucent node is the optical switching fabric. Some ports of
it are dedicated to local tributaries via tunable optoelectronic
devices. Other ports are used by the transiting signals which
can cross the fabric without regeneration or, when needed,
can be switched to the pool of transponders to be regenerated
before leaving the node (see figure 1).
A DWDM line system is composed of a fiber, a set of
optical line Erbium Doped Fiber Amplifiers (EDFA), boosters
and pre-amplifiers together with a wavelength multiplexer
(mux) and a de-multiplexer (demux) at each terminal of
the system. We assume that in our OTN model all optical
links are equipped with the same type of DWDM systems;
a link can host one or more line systems at the same time.
In each DWDM system a preassigned maximum number of
wavelength channels can be lit on and this number is assumed
to be the same for all the systems of the network. We do
not consider other optical-domain processing devices such as
dispersion compensators, WDM channel equalizers, etc. The
physical layer impairment model is based on the computation
of Personick’s Q factor [17][18] described in the deliverable
D2.1 of Nobel project [19] and run by a network simulator
developed in C++. A signal quality threshold Qth has been
used to evaluate whether the optical signal needs regeneration
or not. Note that Qth =17 dB roughly corresponds to a BER
of 10−12 (assuming no FEC performed).
B. Choice of the set of regenerating nodes
The Regenerator Placement Problem (RPP) is a subproblem of translucent network design: it aims at finding the
minimum number of regenerator nodes, their best location and
the number of 3R units so that a communication path can be
established between every pair of source-destination nodes in
the network. RPP has been proved to be NP-complete [15][16].
Let us consider G(N,A) as the physical graph of the network
where N represents the set of nodes and A the set of physical
links. Being understood that the opaque implementation of the
network is when all the nodes have one transponder per transit
lightpath, the translucent implementation of the same network
can be conceived according to two different approaches:
•
Sparse translucent approach: every network node can potentially host 3R units, which means S3R =N . Obviously,
this implies that each node can have 3R units, though not
all the nodes necessarily have transponders at the end of
the next design step;
•
Clustered translucent approach: only a subset of network
nodes are identified as regenerating nodes and are given
the capability of hosting 3R units. All the other nodes are
purely transparent. Thus, S3R ⊂ N .
In this first study we have considered two of several
algorithms proposed in literature to accomplish the first subproblem in a clustered translucent network: Nodal Degree First
DWDM system
W input W
WDM
channeels
Pre - Amplifier
U
OLA
X
Signal OUT
85 Km spacing
W outp
put WDM
chaannels
Booster
M
D
E
M
U
X
Tributaries
Signal IN
All optical
switching fabric
Translucent node
Tunable Transponder (3R unit)
Fig. 1.
Node model and DWDM system for a Translucent network
(NDF) [12] and Central Node First (CNF) [12] algorithms.
Before describing the algorithms, let us introduce an useful
concept in this kinds of problems: we define the transparency
island (TI) [8][9] as the set of nodes that can be reached by
a node along the shortest path without using 3Rs. In order to
connect two nodes that are not in each others TI, regeneration
units are needed at some intermediates nodes.
NDF and CNF follow the same procedure: once the algorithm
starts, nodes are sorted using a certain criterion; then one node
at a time is added to S3R until it becomes a 1-connected and
1-dominating set [10]. This means that: 1) each node which
is not in S3R must belong to the transparency island of at
least one node in S3R (1-dominating); 2) each node in S3R
must belong to the transparency island of at least one node in
S3R in such a way that it can reach every regenerating node
exploiting S3R -nodes’ transparency islands (1-connectivity).
This allows every node in the network to establish at least
one lightpath with any other node. A k-dominating and kconnected set would guarantee at least k 3R node-disjoint paths
between every source-destination pair (this option is left for
future investigation).
NDF sorts nodes in decreasing order on the basis of their
nodal degree and builds the set S3R sequentially picking nodes
from the list. As one node is added to S3R , it is removed
from the sorted list and the nodal degree of all its neighbors
is decremented by one. Then the list is resorted. S3R continues
to grow by adding more nodes to the set until it becomes a
1-connected and 1-dominating set.
CNF algorithm ranks every node using the topological
“centrality”, i.e. a weight proportional to the number of
times a node is crossed by the shortest paths between all the
node pairs in the network; the more a node is crossed the
more central it becomes for the network. S3R is constructed
adding nodes from the list starting with the one having the
highest ranking until the set fulfills the 1-connectivity and 1dominating constraints [10].
C. Quantification of Resources
1
This second step of the translucent
design procedure
is carried out by solving the Routing and Wavelength
Assignment with Regenerator Problem (RWA-RP) [15] for
each connection using a greedy algorithm. Regenerating
nodes are known as output of the previous step and 3R
units are allocated to these nodes only when needed, trying
to minimize their total number in the network. After all
connections have been set up, unused regenerating nodes, if
any, are removed from the original S3R set.
III. S TATIC T RAFFIC D ESIGN R ESULTS
As case-study example, design experiments have been performed using the well known PAN European network (28
nodes, 41 edges) [14]. We have assumed a uniform static
matrix of demands including one bidirectional request for a
10 Gbit/s connection between each pairs of network nodes.
Moreover, the maximum capacity of all DWDM systems is
set at 40 lambdas.
Results of the dimensioning phase in terms of total number
of regenerator nodes, installed 3R units and DWDM systems
are reported in Table I for three different values of Qth . They
show the clear advantage of the translucent approach (both
sparse and clustered) over the opaque one in terms of number
of 3R units, as expected. In some cases sparse translucent
allows slightly reducing also the number of DWDM systems,
thus achieving a clear CAPEX saving. This is due to a better
D ESIGN
S TRATEGY
N UMBER
OF
3R N ODES
N UMBER
3R UNITS
OF
N UMBER
OF
U NIDI RECTIONAL
DWDM
S YSTEMS
Qth = 21dB
OPAQUE
28
4720
118
SPARSE
23
716
112
CNF
19
728
114
NDF
10
874
120
formerly designed for the same given static traffic under
dynamic traffic (assuming a Poissonian model for lightpath
setup and tear-down request generation). Dynamic traffic has
been assumed to be uniform on all the node-pairs, as is the
static traffic employed in dimensioning the network. Figure 2
PAN Network (28 Nodes, 41 Edges, Qth=21dB)
1
Qth = 19dB
28
4960
118
SPARSE
21
354
114
CNF
16
356
122
NDF
5
368
118
Qth = 17dB
OPAQUE
28
4960
118
SPARSE
18
128
118
CNF
2
128
124
NDF
2
132
126
TABLE I
N ETWORK - PLANNING RESULTS WITH STATIC TRAFFIC
-2
10
Blocking Probability
OPAQUE
-4
10
-6
10
-8
10
CNF-based
NDF-based
-10
10
distribution of load in the network compared to the opaque
case, in which routing is purely shortest-path based. Shortestpath routing tends to overload few links, increasing the number
of links in which two, instead of one, DWDM systems must
be deployed.
The clustered translucent approach requires more DWDM
systems than the sparse one, due to the fact that transponder
locations are constrained. Thus, clustering transponders in a
subset of nodes is economically effective only when fullytransparent network nodes have a lower cost than nodes with
the capability of hosting 3R units, so to compensate the
extra CAPEX for more DWDM systems. This scenario can
become realistic when operational costs due to regenerator
hosting are high (e.g. larger area occupation in node-housing
infrastructures, much larger energy consumptions, need for
special equipment for heat dissipation, maintenance costs,
etc.).
Clustered and sparse translucent implementations tend to
converge in terms of number of deployed transponders when
Qth decreases. This leads to an enlargement of the transparency island of each node and to the deployment of more
DWDM systems due to less λ-converters locations in the
newtork. CNF tends to select 3R nodes adjacent one another
and located closer to the “centre” of the network than NDF.
NDF, on the other hand, reduces the cardinality of the set
S3R by placing 3R nodes more scattered over the network
topology, sometimes at the edges of it. In terms of number of
transponders, NDF is more efficient than CNF.
IV. DYNAMIC S CENARIO
If the semi-transparent implementation is less expensive in
terms of CAPEX compared to the opaque, the drawback of the
translucent approach is a worse behaviour in dynamic-traffic
conditions. We have compared translucent and opaque network
SPARSE-based
OPAQUE
-12
10
0.6
0.7
0.8
0.9
1
Offered Traffic for node pair [Erlang]
Fig. 2.
Blocking probability under dynamic traffic condition
shows the results based on a computer simulation in which
the confidence interval is 1% or less at 95% confidence
level. It should be noted that blocking can occur for two
reasons: lack of free resources and lack of free 3R units. The
translucent implementation, no matter if sparse or clustered
and regardless of the algorithm used to build the set S3R ,
exhibits a large blocking-probability gap compared to the
opaque implementation. Similar graphs, which we do not
report here due to space limitations, have been obtained by
carrying out simulations for other values of Qth (i.e. 17 dB,
19 dB).
V. P OWER C ONSUMPTION A NALYSIS
Nowadays the environmental impact of telecommunications
is drawing the attention of the scientific community: energyconsumption minimization is becoming a key target for network design [20]. The translucent approach is intrinsically
“green” as it uses less regenerators than the opaque, thus
reducing the total network power consumption. We have evaluated power saving with different 3R placement algorithms
under static and dynamic traffic scenario. We have assumed the
following power-supply values for the equipment represented
in figure 1. These data have been obtained after an analysis
of device consumption from different vendors:
• 3R unit (tunable transponder at 10 Gb/s): 60 Watt;
• optical line amplifier: 100 Watt;
• mux+demux+booster+pre-amplifier: 280 Watt;
Total Power Consumption in PAN Network
(Qth=19dB) under Static Traffic condition
1000 kW
Power Consumption (kW)
707.7
152.3
145.8
151
100 kW
OPAQUE SPARSE-based CNF-based NDF-based
Used Algorithms
Fig. 3. Power consumption under static traffic condition (one bi-directional
demand for every node pair)
The switching-fabric power consumption has been neglected.
Mean Power Consumption in PAN
Network under Dynamic Traffic
Condition (Qth=19dB)
250 kW
Mean Power Consumption (kW)
160.9
143.8
143.81
149.7
100 kW
73.5
68.7
65.7
59.1
Minimum Traffic Load (0.1 Erlang)
Maximum Traffic Load (1 Erlang)
10 kW
OPAQUE SPARSE-based CNF-based NDF-based
Used Algoritms
Concerning the dynamic scenario, we assumed that devices
could be switched on by a suitable signaling protocol only
when needed, i.e. at lightpath set up, and switched off when a
lightpath is torn down. For each formerly designed network,
we carried out simulations in order to sample the number of
devices simultaneously active (proportional to the istantaneous
power consumption). Average values for Qth =19 dB, at 0.1
and 1 Erlang are shown in figure 4.
At 1 Erlang there is no relevant benefit in power savings if
we take into account that the blocking probability displays a
similar gap as the one in figure 2. This means that the slightly
higher power consumption of the opaque OTN is due to lower
blocking compared to the translucent one (i.e. more accepted
lightpaths imply more active devices at the same time). At 0.1
Erlang, none of the networks experienced blocked connections
during the entire simulation process, which makes them easily
comparable; in this case, a dominant role is played by the
higher number of DWDM systems that are active per link: as
already explained, the shortest-path based opaque network is
less efficient than the translucent approach. Among transucent
OTNs, the one exploiting CNF outperforms that based on
NDF.
VI. C ONCLUSIONS
In this paper we have investigated the performance of
translucent approach compared to the opaque case under static
and dynamic traffic. What emerged from this study was the
CAPEX-saving behaviour of the translucent approach in the
static scenario, particularly with the CNF placement strategy
which showed the best trade/off between the required number
of 3R nodes, 3R units and DWDM systems. Nevertheless,
sparse placement allows further decreasing the quantity of
resources even though increasing the number of regenerating
nodes. In dynamic traffic conditions, opaque implementation
shows better performance due to the inflexible behaviour of the
static-traffic-designed translucent networks: as simulation goes
on, lack of the few resources causes connections to be routed
along different paths than those planned in the dimensioning
phase. This makes traffic demands experiencing blocking most
of the time. These first results suggest us that: 1) more
advanced transponder-clustering algorithms may be needed in
selecting the set S3R in order to preserve the advantages of a
translucent network over opaque also with dynamic traffic; 2)
revenues due to CAPEX-savings should be related to a cost per
blocked connection in the dynamic scenario, in order to have
a global point of view of the translucent vs opaque debate.
These are the purposes of our next studies.
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Fig. 4.
Power consumption under dynamic traffic condition
Results in figure 3 are related to the static traffic situation.
We assume that in static conditions devices are permanently
active and thus power consumption is proportional to the
number of devices that have been installed. In this scenario
the advantages of translucent architecture in terms of power
saving are quite clear.
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