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IEEE COMMUNICATIONS LETTERS, VOL. 16, NO. 6, JUNE 2012
941
Assessment of the Power Series Routing Algorithm in
Translucent, Transparent and Opaque Optical Networks
Joaquim F. Martins-Filho, Senior Member, IEEE, Jamersson L. de Santana, Helder A. Pereira,
Daniel A. R. Chaves, and Carmelo J. A. Bastos-Filho, Senior Member, IEEE
Abstract—In this letter we extend the analysis of the Power
Series Routing (PSR) algorithm, which was previously proposed
and analyzed for transparent networks only, to the translucent
and opaque networks. The PSR algorithm is a bio-inspired
impairment-aware routing algorithm that can be optimized and
can adapt itself to the network conditions. We demonstrate
that the PSR algorithm is also suitable for these network
configurations. We compared the PSR algorithm with other
well known routing algorithms and we observed that the PSR
algorithm achieved the best performance in all cases.
Index Terms—Optical networks, routing and wavelength assignment, regeneration, physical impairments.
I. I NTRODUCTION
I
N dynamic wavelength routed optical networks, the routing and wavelength assignment algorithm (RWA) plays a
crucial role in the network performance [1]. The impairmentaware RWA algorithm (IA-RWA) has to provide lightpaths
with a quality of transmission (QoT) above a predefined
threshold, while reserving resources for future demands [2]–
[5].
The optical networks can be classified as opaque, transparent or translucent [6]. This classification depends on the
usage of regenerators along the network nodes. Electronic
regenerators convert the optical signal to the electric domain in order to perform re-amplification, re-shaping and
re-timing (3 R regeneration), and then convert it again to
the optical domain to transmit the signal to the next link
in the lightpath [1]. In opaque networks, all nodes provide
regeneration for all by-passing lightpaths. On the other hand,
none of the nodes presents the regeneration capability in
transparent networks. In translucent networks, some of the
nodes have a certain number of regenerators, whereas the
other nodes are transparent [1]. Translucent networks can be
viewed as a compromising solution that can achieve a high
transmission performance, such as in opaque networks, but
with a lower cost, such as in the transparent case.
We have proposed an IA-RWA algorithm called Power
Series Routing (PSR) algorithm that can be optimized and can
adapt itself to the network conditions [7] [8]. This is possible
due to the bio-inspired component used to build the PSR. For
transparent optical networks, the PSR algorithm presented a
Manuscript received February 1, 2012. The associate editor coordinating
the review of this letter and approving it for publication was V. Vokkarane.
J. F. Martins-Filho is with the Department of Electronics and Systems,
Federal University of Pernambuco, Recife, PE, Brazil (e-mail: [email protected]).
J. L. de Santana, H. A. Pereira, D. A. R. Chaves, and C. J. A. BastosFilho are with the Polytechnic School of Pernambuco, University of Pernambuco, Recife, PE, Brazil (e-mail: {helder.pereira, danielchaves}@poli.br,
[email protected]).
Digital Object Identifier 10.1109/LCOMM.2012.032612.120232
superior performance in terms of blocking probability when
compared to other well known algorithms, such as optical
signal-to-noise ratio routing (OSNR-R) [9], shortest path or
minimum number of hops. Besides, Chaves et al. [8] showed
that the PSR can automatically adapt itself in the case of
network failures or traffic patterns variations to improve the
network performance.
In this letter, we extend the performance assessment of the
PSR algorithm to translucent, opaque and transparent optical
networks. We compare the PSR algorithm with other well
known routing algorithms in different scenarios. The rest of
the paper is organized as follows. In section II we briefly
present the PSR algorithm. Section III describes the simulation
setup used in the simulations. The simulations results are
presented and discussed in Section IV. Finally, In section V
we give our conclusions.
II. P OWER S ERIES ROUTING A LGORITHM
The PSR algorithm has two stages: the planning phase and
the operational phase. The link cost function used by PSR
algorithm is defined in the planning phase. In the operation
phase, the PSR is used as an IA-RWA algorithm during the
network operation.
The first step of the planning phase is to choose the input
variables for the cost function. Chaves et al. [8] used the
normalized link length and normalized link availability as
the input variables for the cost function. The normalized link
length di,j between the nodes i and j is defined by:
di,j =
i,j
,
max
(1)
where i,j is the length of the link connecting the nodes i and
j and max is the maximum link length in the network. The
normalized link availability ai,j is defined by:
ai,j =
λai,j
λTi,j
,
(2)
where λai,j and λTi,j are, respectively, the number of available
and the total number of wavelengths in the link between nodes
i and j.
In the second step of the planning phase, one needs to
describe the cost function in terms of a series. For two input
variables, the cost function can be described as [8]:
f (di,j , ai,j ) =
c 2012 IEEE
1089-7798/12$31.00 N
N
n0 =0 n1 =0
bn0 ,n1 dni,j0 ani,j1 ,
(3)
942
IEEE COMMUNICATIONS LETTERS, VOL. 16, NO. 6, JUNE 2012
where bn0 ,n1 are the series expansion coefficients and N is the
number of terms used to truncate the infinite series expansion
as discussed in [8].
In the last step, an optimizer is used to determine the series
coefficients in order to minimize the blocking probability.
We used the bio-inspired technique called Particle Swarm
Optimization (PSO) algorithm to perform this optimization as
indicated in [8]. The optimization process can be formalized
as: find the values of the vector bn0 ,n1 that optimize the
network performance indicator chosen as the optimization
target, in our case, the blocking probability obtained from
offline simulations of the network, prior to its operation. The
optimization of the function parameters in the planning phase
takes into account the physical impairments, which gives to the
PSR the characteristics of an IA-RWA algorithm. Moreover,
the computational complexity of the online operation of the
PSR algorithm consists only of gathering the values of the
selected variables chosen and the evaluation of a relatively
simple function as shown in (3), the cost function.
III. S IMULATION S ETUP
We used a version of the SIMTON simulator [10] for
translucent networks to assess the network performance both
in the planning and in the operation phases. The SIMTON
simulator uses the physical layer model proposed by Pereira
et al. [9] that quantifies the OSNR degradation and the
pulse broadening along the optical signal propagation in
an all-optical lightpath. The following physical impairments
were considered in the simulations: amplified spontaneous
emission (ASE), polarization mode dispersion (PMD), residual chromatic dispersion (CD) and homodyne crosstalk. For
translucent networks, we considered the sparse regeneration
approach to perform the translucent design. We used a well
known regenerator placement (RP) algorithm, called node
degree first (NDF) [4] to determine which nodes will present
regeneration capabilities and define the number of regenerators
per node in the case of translucent network.
Fig. 1 shows the network topology used in our simulations.
Three scenarios are investigated: transparent, translucent and
opaque networks. For the transparent scenario, the network
nodes are not provided with regenerators. For the translucent
scenario, the NDF algorithm is used to perform the regenerator
placement. The numbers inside the network nodes in Fig. 1
indicate the amount of regenerators placed by the NDF in each
network node. The NDF algorithm was ran with the following
input parameters: 8 translucent nodes and 12 regenerators per
node. For the opaque scenario we considered that each node
has an unlimited number of regenerators.
The traffic follows the dynamic lightpath establishment
(DLE), where the call arrivals follow a Poisson’s process
and the call hold time follows an exponential distribution.
The source-destination pairs of each call are chosen randomly
(uniform distribution). If both the OSNR of a given candidate
lightpath is above 20 dB and maximum pulse broadening is
below 10 % of the bit slot, then the call is accepted. Otherwise,
the request is rejected. An accepted call results in the establishment of a circuit switched bidirectional connection in two
different fibers between the source and destination nodes. The
0
90
0
140
120
100
12
90
0
90
100
12
100
80
0
0
12
100
100
90
0
90
90
0
12
120
150
150
100
12
100
12
12
60
110
12
0
100
100
0
Fig. 1. Network topology. The numbers inside the nodes indicate the number
of regenerators of the translucent network used in the simulations and the
numbers in the link indicate the link length in km.
blocking probability is obtained from the ratio of the number
of blocked calls and the number of call requests.
We used the following optical parameters with the Pereira’s
et al. model [9] to obtain the simulations results: Transmission fiber loss coefficient of 0.2 dB/km, zero dispersion
2
wavelength of 1450 nm, dispersion
√ slope of 0.045 ps/km.nm ,
PMD coefficient of 0.04 ps/ km, dispersion coefficient
and slope for DCF fibers, respectively, of −110 ps/km.nm
and −1.87 ps/km.nm2 at 1550 nm, transmitters linewidth of
0.013 nm, input optical power of 3 dBm, input OSNR of 40 dB,
bit rate of 40 Gbps, wavelength grid starting at 1528.77 nm
(the residual dispersion is zero at 1528.77 nm), switch isolation factor of −38 dB, optical filter bandwidth of 100 GHz;
amplifier noise figure 5.5 dB; MUX and DEMUX loss of 2 dB.
IV. R ESULTS
We compare our approach to other well known routing algorithms: Shortest Path (SP), Minimum Number of
Hops (MH), Least Resistance Weight (LRW) and OSNR-based
routing (OSNR-R) (references for these algorithms can be
found either in [8] or [9]). In all cases we used the first fit (FF)
as the wavelength assignment algorithm.
Fig. 2, 3 and 4 show the blocking probability as a function
of the network load for the PSR and the other RWA algorithms
using (a) 12, (b) 24 and (c) 36 wavelengths per link for
transparent, translucent and opaque scenarios, respectively.
For the transparent network with 12 wavelengths per link,
the algorithms present similar performance because the network is strongly limited by the lack of resources (wavelengths)
and the routing algorithms perform equally poorly. For 24 and
36 wavelengths per link, some routing algorithms can find
the least impaired routes, achieving the best performance. Although the PSR presented the best performance, the blocking
probability is very similar to the OSNR-R and SP algorithms
in these cases. Note that the blocking probabilities are very
similar in Fig. 2(b) and in Fig. 2(c). In this case, increasing
of the number of wavelengths per link does not reduce the
blocking probability because the transparent network is limited
by the physical impairments.
MARTINS-FILHO et al.: ASSESSMENT OF THE POWER SERIES ROUTING ALGORITHM IN TRANSLUCENT, TRANSPARENT AND OPAQUE OPTICAL . . .
(a)
(b)
943
(c)
Fig. 2. Blocking probability of a transparent network as a function of the network load for the PSR and other RWA algorithms using: (a) 12, (b) 24 and
(c) 36 wavelengths per link.
(a)
(b)
(c)
Fig. 3. Blocking probability of a translucent network, with the regenerators placed as shown in Fig. 1, as a function of the network load for the PSR and
other RWA algorithms using: (a) 12, (b) 24 and (c) 36 wavelengths per link.
(a)
(b)
(c)
Fig. 4. Blocking probability of an opaque network as a function of the network load for the PSR and other RWA algorithms using: (a) 12, (b) 24 and
(c) 36 wavelengths per link.
For the translucent (Fig. 3) and opaque (Fig. 4) scenarios,
one can observe that the PSR algorithm far outperformed all
the other RWA algorithms, specially when the opacity of the
network increases. It probably occurs due to the adaptation
capacity of the PSR algorithm already presented in [8] (limited
to transparent scenarios in [8]). This adaptation capacity is a
direct result of the offline planing phase of the PSR algorithm,
when the best series coefficients bn0 ,n1 of (3) are found
considering the network topology, including the number of
available wavelengths and regenerators. Therefore, the PSR
performs better, relative to the others, for translucent and
opaque scenarios because it can make better use of the network
resources (wavelengths and regenerators).
Moreover, note that the PSR algorithm is robust to deal with
different scenarios, it performs as good as (or better than)
the best algorithm for all investigated scenarios. The other
algorithms do not show this characteristic. For instance, the
OSNR-R shows a high performance for transparent networks
whereas it performs poorly for the translucent and opaque
cases.
V. C ONCLUSION
This paper presented the first analysis of the Power Series
Routing algorithm in translucent and opaque optical networks.
We compared the PSR algorithm in three different scenarios
for translucent, opaque and transparent networks. Our results
944
IEEE COMMUNICATIONS LETTERS, VOL. 16, NO. 6, JUNE 2012
show that the PSR algorithm outperformed all the other RWA
algorithms, specially for translucent and opaque networks, in
which its blocking probability can be more than one order of
magnitude lower than the others. Although the PSR algorithm
had been developed initially for transparent networks, it presented even better results for translucent and opaque networks,
because it could adapt itself to the network conditions, making
better use of the network resources (available wavelengths and
regenerators).
ACKNOWLEDGMENT
The authors thank FACEPE, CNPq, UPE and UFPE for
scholarships and grants.
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