Download Dynamic RWA for All Optical Networks Using

Dynamic RWA for All Optical Networks Using Linear Constraints for
Optical Path Feasibility Assessment
Svetoslav Duhovnikov, Dominic A. Schupke, Gottfried Lehmann, Thomas Fischer, Franz Rambach
Siemens AG, Corporate Technologies, Information & Communication, Optical Transmission Networks
Otto-Hahn-Ring 6, 81730 Munich, Germany, [email protected]
Abstract We propose a fast and simple RWA algorithm with physical constraints which are modeled by linear
restrictions based on wavelength groups, length, and hops. Simulation results indicate blocking performance
close to unconstrained RWA.
Introduction
The emergence of networks with dynamic WDM
connection provisioning can be foreseen in the future.
There exists the problem that the feasibility of the
candidate paths for the new connection and their
impact on the other channels present in the system
has to be determined.
We propose a Routing and Wavelength Assignment
(RWA) algorithm for transparent optical Wavelength
Division Multiplexing (WDM) networks (subject to
wavelength-continuity constraint) considering the
physical constraints imposed by the specific
characteristics of the optical fiber [1,2]. It is a modified
shortest available path routing algorithm taking into
account the performance of the individual optical
channels.
Algorithm description
The approach essentially consists of three steps.
First,
the
wavelength-dependent
transmission
performance in transparent NRZ-OOK modulated
WDM networks with Single Mode Fiber (SMF) is
assessed for all the wavelengths. This calculation is
based on a comprehensive set of analytic models
known from the literature. All transmission effects,
which are relevant for bitrates of 10Gbit/s, are
included: ASE, PMD, GVD, SPM, XPM, FWM and
SRS. Simultaneously for all channels, the results of
the individual models are consolidated to overall
performance results measured by Q-factors.
Networks with a line topology are considered that are
fully loaded 40 wavelengths, equal span length of 80
km and a total length of 2960 km (37 spans). The
nodes are placed at both ends of the network and
randomly onto the possible intermediate locations
(nodes can be only placed at a span end). For every
path between a node pair, Q-factor values are
computed.
Second, from the obtained Q-factors, linear
constraints are derived. They allow for simple
implementation and fast verification of candidate path
feasibility.
Third, a standard shortest available transparent path
(SATP) algorithm is modified to include the derived
linear constraints. To verify the results of the
algorithm, the blocking probability is simulated and
compared with other algorithms that do not consider
physical constraints
Constraints derivation and wavelength groups
Figure 1 presents the collected Q-factors, which are
plotted in a graph with the x-axis representing the
number of hops and the y-axis the length of the path.
One should keep in mind that the hops displayed on
the x-axis are only intermediate nodes. Every point
represent a [hops, length] pair for a particular path.
Multiple outcomes for a single point are possible,
because of the random node placement. The discs
depict feasible paths (Higher Q-factor) and the circles
show unfeasible paths (Lower Q-factor).
Figure 1: Q-factor values with given threshold for a
wavelength group
The aim is to locate a region, where all the Q-factors
are above the threshold and based on this region
derive a maximum number of hops, a maximum
length and a straight line which depends on the hops
and the length (more than one mixed constraint can
be found for better precision). Furthermore we divide
the wavelengths into 4 groups with similar
performance resulting in the following constraints, see
Table 1 and 2:
Table 1: Hops and Length constraints for a path
Group
Max. Hops
Max. Length, km
1
8
2000
2
8
2320
3
9
2640
4
12
2960
SATP-LW is used the blocking probability is nearly
constant and at an unacceptably high level. The
worst-wavelength assumption is therefore too
stringent and this suggests that the wavelength
assignment should consider the Q-factor performance
of the individual wavelengths (SAPT-WD) as
discussed in [4].
Table 2: Joint length and hops constraints for a path
Group
Mixed Constraints
1
− 120 * Hops + 2000 ≤ PathLength
2
3
4
− 110 * Hops
− 110 * Hops
− 144 * Hops
− 480 * Hops
+ 2320
+ 2750
+ 3824
+ 7520
≤ PathLength
≤ PathLength
≤ PathLength
≤ PathLength
Simulation setup
In our scenario we consider the following routing
algorithms: without constraints - shortest transparent
path (STP) and SATP; with constraints - SATP with
wavelength dependent performance (SAPT-WD),
SAPT with all wavelengths assumed to perform like
the wavelength with the highest Q-factor (SAPT-BW)
and SATP with all wavelengths assumed to perform
like the wavelength with the lowest Q-factor (SAPTLW). The SAPT-WD chooses the minimal reach
available wavelength that can cover the distance
demanded by the connection.
For STP the routes are pre-computed, while for the
other algorithms a new path search is performed
based on the current available wavelengths. The
wavelengths are assigned by “first fit.”
To evaluate the network performance, measured by
the ratio of rejected connections over routed
connections (blocking probability), we compare the
above mentioned algorithms with the following setup:
•
Network topology: Network of national size
with X nodes and Y links designed for a
static demand matrix
•
40 system wavelengths;
•
Dense demand matrix;
•
Demand
inter-arrival
time:
Negative
exponentially distributed with mean of 10
time units [3];
•
Demand service time: constant at 1 time
unit;
•
Confidence level: 95%;
•
Confidence interval: ±1% relative to mean.
Figure 2 compares the blocking probability of the
different routing algorithms as a function of scaled
demand matrix, i.e. we scale the offered traffic
volume by the x-axis value.
When SAPT-BW is used the blocking is close to the
SATP, which is unconstrained. On the other hand, if
Figure 2: Network performance given different routing
algorithms
The SAPT-WD has significantly lower blocking
probability than the simple STP routing, which can be
designed to automatically yield physically feasible
paths, and slightly higher blocking probability
compared to the SAPT.
Further investigation is needed to include unequal
spans lengths, which is the case for all real networks,
into the model for Q-factor calculations.
Conclusions
In this paper, we have examined the dynamic RWA
problem in the presence of the physical constraints
imposed by the optical fiber characteristics. Based on
linear constraints, derived for a set of wavelength
groups given optical signal quality measured by Qfactor, candidate paths for a connection demand can
be checked for feasibility.
The performed simulations indicate that the network
performance, measured in blocking probability, using
SAPT-WD
is
comparable
with
the
SATP
(unconstrained) and SATP-BW, while it significantly
outperforms the SATP-LW.
References
1 Zang et al., Optical Networks Magazine, Jan. 2000.
2 Strand et al., IEEE Communications Magazine,
Feb. 2001.
3 Schupke et al., DRCN, 2003.
4 Lehmann et al., APOC, 2005.