AN MINIMUM RECONFIGURATION PROBABILITY ROUTING ALGORITHM FOR RWA IN ALL-OPTICAL NETWORKS

Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.6, December 2016
DOI : 10.5121/sipij.2016.7604 39
AN MINIMUM RECONFIGURATION
PROBABILITY ROUTING ALGORITHM FOR
RWA IN ALL-OPTICAL NETWORKS
Mohan Kumar S1
and Jagadeesha S N2
1
Ph.D, Research Center, Jawaharalal Nehru National College of Engineering, Shimoga,
Karnataka, India
2
Department of Electronics and Communication Engineering, PES Institute of Technology and
Management, shivamogga. Karnataka, India
ABSTRACT
In this paper, we present a detailed study of Minimum Reconfiguration Probability Routing (MRPR)
algorithm, and its performance evaluation in comparison with Adaptive unconstrained routing (AUR) and
Least Loaded routing (LLR) algorithms. We have minimized the effects of failures on link and router failure
in the network under changing load conditions, we assess the probability of service and number of light
path failures due to link or route failure on Wavelength Interchange(WI) network. The computation
complexity is reduced by using Kalman Filter(KF) techniques. The minimum reconfiguration probability
routing (MRPR) algorithm selects most reliable routes and assign wavelengths to connections in a manner
that utilizes the light path(LP) established efficiently considering all possible requests.
KEYWORDS
Routing Wavelength Assignment (RWA), Blocking Probability(BP) Network Management(NM), Kalman
Filter(KF), Wavelength Interchange(WI), Light Path(LP), Process Noise(PN), Least Loaded Routing(LLR),
Minimum Reconfiguration Probability Routing (MRPR), Adaptive Unconstrained Routing(AUR)
1. INTRODUCTION
In this paper, we have considered Minimum Reconfiguration Probability Routing algorithm for
Routing and Wavelength Assignment (RWA) for Wavlength Interchange(WI) network. It has
scalable architecture and has mesh like structure consisting of links having one or more fibers at
each input port to output port in the optical domain. The challenging problem in these networks
are Routing and Wavelength Assignment and controlling problems. In these problems, provision
of connections, called lightpaths in a scalable architecture usually span multiple links. Hence,
light path might be assigned to different links along its route. This process is called Routing and
Wavelength Assignment. In this process, a lightpath shares one or more fibers links and different
wavelengths. To establish a lightpath, a route should be discovered between source and
destination, and suitable wavelength need to be assigned to that route. Some of the commonly
used performance criteria for Routing and Wavelength Assignment are throughput and blocking

40
probability[10]. There are many RWA algorithms proposed for this purpose with optimal
solutions[10]. The main assumption of these algorithms are that, traffic volume is static for a
long time period and these networks are reconfigured only to reflect changes in the long term
traffic demand[1]. Although static demand has been reasonable a assumption for voice data
communication. In current trends and future, data intensive networks are rapidly changing.
Therefore dynamic RWA algorithms which support request arrivals and lightpath terminations at
stochastic/randaom times are needed. Hence, predefined set of routes are searched in a predefined
order to accommodate the request. Then the smallest index randomly selects a wavelength
available on the route. If not, request is blocked. Usually one or more minimum hop routes are
used and fixed order search is carried out without taking into account, the congestion on the link.
In this paper, we present the comparative study and performance evaluation of Minimum
Reconfiguration Probability Routing (MRPR) algorithm, over Adaptive Unconstrained Routing
(AUR) and Least Loaded Routing (LLR) methods.
An Optical switch has ability to minimize the effects of failures on network performance by using
a suitable routing and wavelength assignment method without disturbing other performance
criteria such as network management and blocking probability[4]. The main challenge in the
Wavelength Interchange (WI) and Wavelength Routing Network (WRN) are the provision of
connections called lightpaths between the users of network. One of the Routing Wavelength
Assignment method known as Minimum Reconfiguration Probability Routing algorithm is
implemented [3][15]. Its performance comparison study over AUR and LLR are presented. This
algorithm makes use of the network state information at the time of routing to find the optimum
path. By choosing as much reliable router/links as possible in the RWA process, it is possible to
minimize the mean number of light paths broken due to failure. However, considering only
reliability characteristics, lightpaths may have to be routed on longer routes and blocking
performance may be reduced significantly. For this reason, the algorithm is based on the joint
optimization of the probability of reconfiguration due to router/link failures and probability of
blocking for the future requests. Therefore effect of potential router/link failures and blocking
probability is measured and also minimized without disturbing performance. Hence, lightpath
arrival/holding time and failure arrival statistics collected for each link and router, as well as the
network state information collected and updated using kalman filter methods at the time of
request arrival are used in routing decisions. That is, the behavior of the network is predicted by
current state information and statistics of the past, to assign the most reliable path to the lightpath
requests[9]. The routing algorithms proposed so far for optical networks uses present state of
the network[3]. Using this information most of authors proposed wavelength pair routing and
wavelenght assigment function. An adaptive RWA algorithm makes use of the network state
information at the time of routing to find the optimum path. Least loaded routing(LLR) and
Adaptive Unconstrained Routing (AUR) are the examples for adaptive RWA process[3].
In the following section, we present a MRPR for routing and wavelength assignment for the most
reliable route-wavelength pair for a light path request. It uses the lightpath request arrival
statistics between the routers, and failure arrival statistics for links and Kalman filter employed on
router/linker to predict network state and the reconfiguration probability of route-wavelength
pairs, and hence selects the most reliable route-wavelength pair. Finally a comparative study and
performance are presented in the section 3.0. The results and concluding remarks and directions
for future work are presented in the section 4.0 and section 5.0.

41
2. MRPR IN WI NETWORKS
In Wavelength Interchange networks, wavelength routers have a full set of wavelength converters
at the output ports and they are able to change the wavelength over every light path passing
through it. Hence, blocking of requests leading to wavelength conficts, can be measured and
reduced significantly[15]. Wavelength router can also change the wavelength, it optimally reduce
the ligth path routing problem after wavelength assignment problem is solved.
The minimum reconfiguration probability routing algorithm is statistically predictive optimal
routing and wavelength assignment algorithm because; it aims to assign the most reliable route-
wavelength pair for a lightpath request. It uses the lightpath request arrival and failure statistics
information from the routers, and predicts the reconfiguration probability of route-wavelength
pairs, and selects the most reliable route-wavelength pair. The algorithm starts by assigning a cost
value to each network element (router/link) and a multiple shortest path computed, then an
optimal shortest path is used as the most reliable path. To find the shortest route with minimum
reconfiguration probability of light path request for reliable path for share per node (SpN)
network, we use the Bellman Ford algorithm to find the shortest path. A kalman filter is
employed at each router/link to retrieve the state of network and minimum reconfiguration
probability of light path request for reliable path reconfiguring for the WI network. The following
two sections determine these costs, and its route, a lightpath through the network.
2.1 COST DETERMINATION
Let ‘x’ be a random variable representing failure inter-arrival times, ݂௜௝ሺ‫ݔ‬ሻ be the probability
density function of failure inter-arrival times for link (i, j), y be a random variable representing
lightpath holding times and ℎ௦ௗሺ‫ݕ‬ሻ be the probability density function of lightpath times for the
router/link pair from source to destination (s-d). It is possible to evaluate the probability of failure
of lightpath of the link (i, j), between routers source‘s’ and destination ‘d’ using these parameters.
Let ܲ௦ௗ
௜௝
be the probability of failure of lightpath in link (i, j) between router source and
destination. It is given by:
ܲ௦ௗ
௜௝
= ‫׬‬ ሺ‫׬‬ ℎ௦ௗሺ‫ݕ‬ሻ݂௜௝ሺ‫ݔ‬ሻ݀‫ݔ݀ݕ‬ ሻ
௬
௫ୀିஶ
∞
௬ୀିஶ
(1)
It is assumed that the failure arrival method is memory-less, hence it is the probability of failure of
lightpath of the link (i, j) in the interval (t, t+dt). It will not fail prior to the time t, hence it is
independent of time t.
Rewriting the equation (1).
ܲ௦ௗ
௜௝
= ‫׬‬ ൣ‫׬‬ ݂௜௝ሺ‫ݔ‬ሻ
௬
௫ୀିஶ
݀‫ݔ‬൧
∞
௬ୀିஶ
ℎ௦ௗሺ‫ݕ‬ሻ݀‫ݕ‬ = ‫׬‬ ݃ሺ‫ݕ‬ሻℎ௦ௗሺ‫ݕ‬ሻ݀‫ݕ‬
∞
௬ୀିஶ
(2)
Tchebycheff Inequality[21], is a measure of the concentration of a random variable near its mean
µ and its variance v= σ2
, and can be used to find the upper bound for equation (2).
ܲ௦ௗ
௜௝
ሾ‫ݔ‬ − ߤሿ ≥ ε} = ‫׬‬ ݂௜௝ሺ‫ݔ‬ሻ݀‫ݔ‬ +
௫ିఓ
ିஶ
‫׬‬ ݂௜௝ሺ‫ݔ‬ሻ݀‫ݔ‬ ≤
∞
௫ାఓ
௏
ఌమ
(3)

42
Where ε>0 and the ݂௜௝ሺ‫ݔ‬ሻ is symmetric around its mean value.
Hence,
݃ሺ‫ݕ‬ሻ = ‫׬‬ ݂௜௝ሺ‫ݔ‬ሻ݀‫ݔ‬ ≤
௏ೞ೏
೔ೕ
ଶሺఓೞ೏
೔ೕ
ି௬ሻమ
௬
௫ୀିஶ
(4)
Using equation (4) and equation (2), we get
ܲ௦ௗ
௜௝
≤ ‫׬‬
௏ೞ೏
೔ೕ
ଶሺఓೞ೏
೔ೕ
ି௬ሻమ
∞
௬ୀିஶ
ℎ௦ௗሺ‫ݕ‬ሻ݀‫ݕ‬ (5)
The estimation of upper bound is as follows
The mean of g(y) is given by
‫ܧ‬ሼ݃ሺ‫ݕ‬ሻ} = ‫׬‬ ݃ሺ‫ݕ‬ሻℎሺ‫ݕ‬ሻ݀‫ݕ‬
ஶ
ିஶ
≅ ݃ሺߤሻ + ݃ᇱ ሺߤሻ
௏
ଶ
(6)
By using equation (6) in equation (2), we get
ܲ௦ௗ
௜௝
≤ ‫׬‬
௏ೞ೏
೔ೕ
ଶሺఓೞ೏
೔ೕ
ି௬ሻమ
∞
௬ୀିஶ
ℎ௦ௗሺ‫ݕ‬ሻ݀‫ݕ‬ ≅
௏ೞ೏
೔ೕ
ଶሺఓೞ೏
೔ೕ
ିఓ೓
ೞ೏
ሻమ
+
ଷ௏ೞ೏
೔ೕ
௏ೞ೏
ೞ೏
ଶሺఓೞ೏
೔ೕ
ିఓ೓
ೞ೏
ሻర
(7)
After simplification, we get:
ܲ௦ௗ
௜௝
≤
௏ೞ೏
೔ೕ
ଶሺఓೞ೏
೔ೕ
ିఓ೓
ೞ೏
ሻమ
൤1 +
ଷ௏೓
ೞ೏
ሺఓೞ೏
೔ೕ
ିఓ೓
ೞ೏
ሻమ
൨ (8)
From the above, the link (i, j) failure probability is given by,
ܲ௦ௗ
௜௝
= ቐ
1
݉݅݊ ቊ1,
௏ೞ೏
೔ೕ
ଶቀఓೞ೏
೔ೕ
ିఓ೓
ೞ೏
ቁ
మ ቈ1 +
ଷ௏೓
ೞ೏
ቀఓೞ೏
೔ೕ
ିఓ೓
ೞ೏
ቁ
మ ቉ቋ
௜௙ ௟௜௡௞ሺ௜,௝ሻ௜௦ ௙௨௟௟௬ ௢௖௖௨௣௜௘ௗ ௢௥ ௕௥௢௞௘௡
௢௧௛௘௥௪௜௦௘
(9)
And similarly, the router jth
failure probability is given by
ܳ௦ௗ
௜௝
= ቐ
1
݉݅݊ ቊ1,
௏೑
೔ೕ
ଶሺఓೞ೏
೔ೕ
ିఓ೓
ೞ೏
ሻమ
൤1 +
ଷ௏೓
ೞ೏
ሺఓೞ೏
೔ೕ
ିఓ೓
ೞ೏
ሻమ
൨ቋ
௜௙ ௡௢ௗ௘ ௝ ௛௔௦ ௙௔௜௟௘ௗ,
௢௧௛௘௥௪௜௦௘
(10)
From the above method, the cost of using link (i,j) and router j for a lightpath from source to
destination can be written as:
‫ܥ‬௦ௗ
௜௝
= − ln൫1 − ܲ௦ௗ
௜௝
൯ , ‫ܥ‬௦ௗ
௝
= − ln൫1 − ܳ௦ௗ
௜௝
൯, (11)

43
2.2 Routing decision for MRPR, LLR and AUR techniques
In the previous section ܲ௦ௗ
௜௝
and ܳ௦ௗ
௜௝
represent the estimated failure probabilities. Then the
൫1 − ܲ௦ௗ
௜௝
൯ and ൫1 − ܳ௦ௗ
௜௝
൯ gives the probabilities of links (i, j) and route j are considered for
routing a lightpath between source s to destination d. Here, probabilities are estimated for each
network element independently. Hence, finding the shortest the path between router s and
destination d in terms of cost can be written as:
‫ܥ‬௦ௗ
௜௝
= − lnሾ൫1 − ܲ௦ௗ
௜௝
൯ ൫1 − ܳ௦ௗ
௜௝
൯] (12)
‫ܥ‬௜௝ = ‫ܥ‬௦ௗ
௜௝
+ ‫ܥ‬௦ௗ
௝
(13)
Suppose if all link cost are equal, then the AUR and LLR routing approach will be adopted for
routing decision. The kalman filter method is well suited for distributed operation since, it is
possible to build an algorithm based on distributed routing algorithm. In this study, routers collect
the failure statistics for present state, future prediction, updation of network state, their adjacent
links and collects the arrival statistics from the router. Hence, router can initiate the routing
algorithm with parameters’ ߤ௛
௦ௗ
and ‫ݒ‬௛
௦ௗ
.
Suppose if wavelength converters are employed in routers, a wavelength can be assigned to a
lightpath randomly or statistically at each link. This approach is called statistically predictive and
optimal wavelength routing. It is also called Minimum Reconfiguration Probability Routing. The
two possibilities for wavelength selection are first fit or random. In first fit wavelength
assignment, the smallest index wavelength is chosen. In random wavelength assignment, one of
the available wavelengths can be selected. First fit assignment methods are reported to be better
than random methods and it is used in most of RWA algorithms [19].
A Markov Decision Process is used in WI networks to estimate the average number of future
rejections on a router. The total cost is the estimation of the Mean number future rejections
requests in the WI network. By accommodating the MRPR algorithm in the router, the statistics
related lightpath arrival/holding time, failure arrival statistics for each link and routers, network
state information at the time of request arrival are used in routing decision.
After solving the routing problem, wavelengths on the links along the route can be assigned
randomly. In order to find the route with minimum reconfiguration probability for a lightpath
request, a simple auxiliary graph G= (N,E),where nodes N represent the routers and each directed
edge(i,j), E represents the link (i,j) from router i to router j, is constructed. Then, cost of each
edge(i,j) is set to:
‫ܥ‬௜௝ = − ln൫1 − ‫ܨ‬௜௝൯ − ln൫1 − ‫ܨ‬௝൯ , − ln൫1 − ܴ௜௝൯ (14)
Where, Fij is the probability of reconfiguration due to failure of the link (i,j) and Fj is the
probability of reconfiguration due to failure on router j for the lightpath to be routed and ܴ௜௝ is
the probability of reconfiguration due to repacking on the link (i j). In equation (14), Edge cannot
be used which means that the link (i,j) has no free wavelength channel at the time of
routing[3][15], if the lightpaths assigned are uniformly distributed over the wavelength in link
(i,j) (i.e arrival rates of lightpaths at different wavelenghts are the same).

44
Wavelength converters are exclusively expensive devices and hence it is treated as routers or
switches. Therefore, some router architectures required Share per Node (SpN) and share per link
method. Share per node architectures (network) are limited numbers of converters, among the
lightpaths, in these networks, light path requests arrive at each routers randomly, and not blocked,
they stay on the network for a random amount of time. It is assumed that, each links/router may
fail at random times, so failures are re-routed on new path [14]. Hence, most of authors proposed
share a limited channel number based WI routers. Although MRPR can be employed for the WI
architectures. MRPR routing method may cause blocking of future requests, which need
wavelength conversion at the router. Therefore, we consider the repacking of lightpaths, which
are using converters in a router. For this purpose, we use converter usage statistics such as mean
arrival time rate and mean holding time of light paths. Establishing a light path between the
routers source ‘s’ and destination ‘d’ in SpN network can be constructed as follows.
• For each wavelength w=1..W and router n, create the nodes ݅௡௪ and ‫݋‬௡௪ which represent
the input and output ports of the routers n at wavelength w respectively.
• For each wavelength v, w=1..W and routers n create the edges (݅௡௩, ‫݋‬௡௪) corresponding
to wavelength conversion in the router n,
• For each wavelength w=1..W and each link (m, n) from router m to router n, create the
directed edges (‫݋‬௡௪, ݅௡௪) which represent the channels at wavelength w in the link (m, n).
• Create the nodes ‫ݎ‬௦ and ‫ݎ‬ௗ corresponding to source and destination terminals for the
lightpath
• For each wavelength w=1..W create the edges (‫ݎ‬௦, ݅௦௪) and (‫݋‬ௗ௪, ‫ݎ‬ௗ)
After construction of graph for a share per node to route a lightpath from a source to destination
are illustrated as shown in Figure (2). The graph is constructed for v, w=1, 2,..W and each router
n and m, the cost of edges (‫ݎ‬௦, ݅௦௪) and (‫݋‬ௗ௪, ‫ݎ‬ௗ) as shown in Figure (2). for w=1..W are set to
zero. The cost of each edge (‫݋‬௡௪, ݅௡௪) are set to:
‫ܥ‬௠௡௪ = ቄ
∞
− lnሺ1 − ‫ܨ‬௠௡ሻ − lnሺ1 − ܴ௠௡ሻ
௡௢ ௙௥௘௘ ௖௛௔௡௡௘௟ ௔௧
௪௔௩௘௟௘௡௚௧௛ ௪ ௢௡ ሺ௡,௠ሻ
௢௧௛௘௥ ௪௜௦௘
ሺ15ሻ
Cost of each edge (݅௡௩, ‫݋‬௡௪), for v ≠ w,
ܺ௡௩௪ = ቄ
∞
− lnሺ1 − ‫ܨ‬௡ሻ − lnሺ1 − ܴ௡ሻ
݊‫݋‬ ݂‫݁݁ݎ‬ ܿ‫ݏݎ݁ݐݎ݁ݒ݊݋‬
݅݊ ݊ ‫ݎ݋‬ ݊ ℎܽ‫ݏ‬ ݂݈ܽ݅݁݀,
‫ݐ݋‬ℎ݁‫ݎ‬ ‫݁ݏ݅ݓ‬
ሺ16ሻ
And the cost of each edge (݅௡௪, ‫݋‬௡௪)
ܺ௡ఓ = ቄ
∞
− lnሺ1 − ‫ܨ‬௡ሻ
௡ ௛௔௦ ௙௔௜௟௘ௗ
௢௧௛௘௥ ௪௜௦௘
ሺ17)
Figure 1 : A share per node network

45
Where ‫ܨ‬௡ and ‫ܨ‬௠௡ are the probabilities of reconfiguration due to failure on the WI router n and
link (m, n) respectively, ܴ௠௡ is the probability of reconfiguration due to repacking on the link
(m,n) and ܴ௡ is the probability of reconfiguration due to repacking for a lightpath using a
converter on the router n which is obtained from the equation (15).
ܴ௡ =
ாሺ௫,ఘሻ
௫.ாሺ௫బ,ఘሻ
(18)
Similarly, LLR and AUR methods are applied to WI network to find out the route and reliable
path of network. Routing in optical networks is simply inherited from traditional circuit
switching. Among all the routing algorithms, the Least Load Routing (LLR) is the most popular
one in traditional circuit switched networks [22]. Naturally there were also attempts at studying
LLR in the context of optical networks in; however, it has only been applied in two types of
optical networks: a network with no wavelength conversion and a network with full wavelength
conversion (i.e. every node has a wavelength converter). An LLR-based routing algorithm in
optical networks with wavelength conversion neither has been formulated nor studied to the
extent of our knowledge. In AUR algorithm, all paths between source and destination are
considered in routing decisions. In LLR all paths between source and destination considered
based on least cost and routing decision will be taken.
AUR is fundamentally different from LLR, in that, it is not limited to a set of predefined search
sequence. The optimal lightpath is obtained by finding the shortest path in WI networks, both do
not use convertor.
Figure 2 : Share per node graphical representation
3. PERFORMANCE EVALUATION
In order to evaluate the performance of network, we have considered the network shown in the
Figure (3) having 6 routers and 13 links. All the links are unidirectional light paths and are

46
established between the source to destination for each request. A Light path request arrives at a
time of each request as a poisson process, and lightpat holding times are assumed to be
exponential process. Each link is composed of single fiber length with three wavelength on each
fiber link. We deploy kalman filter at link/routers to make use of the network state information at
the time of routing to find the optimum path for the request. Light path requests arrive at the
network as a poisson process of rate ߣ் and are uniformly distributed from source to destination
over the routers and routing decisions are made randomly for a lightpath request. The lightpath
holding times are exponentially distributed with unit mean. The blocked lighpath requests are
assumed to be lost.
Figure 3: Assued topoogy having 6 routers and 13 links with single fiber
In order to assess the performance of MRPR, AUR and LLR, we consider two different types of
routers characterised as reliable routers and un-reliable routers. The failures arrival at each
reliable path or route, In all the computer simulations, the mean arrial of request rate and mean
holding time are assumed to be poission processes of rates 0.0001 and 0.01 respectively. The
service begin time, service elsaped time, serive ended and customer waiting time have been
taken in to account in order to make routing decisions and reconfiguration of the network. We are
interested in (1) blocking probability of a light path request (2) probability of re-routing due to
link or router failure. We also measure the performance under changing load and reliablity
conditions. The objective function is given by
ߣ =
ߣ் ‫ܪ‬
ܹ‫ܮ‬
ሺ19ሻ
Where ߣ் is the total network load, and H is the average number of fibers per link per path. W is
the number of wavelength per fiber and L is the number of fibers used in WI network.
The performance of network purely depends on reconfiguration and blocking performance with
different reliablity and load values. Hence, for each load and reliabilty ratio values, we carried out
10 simulations.
In order to compute repacking probability, we need to know the initial arrival rate of lighpath and
load offered to network for each resource. It is very difficult to determine actual offered load in
the network, hence, we use light path arrival rates on each resource or service begin at network
for link/router as its initial capacity (numbers of wavelength available on link/router). Then the
number of occupied wavelengths are scanned every δ times and offered loads are updated using φ
kalman functions.

47
For i= 1 to φ
݊௥ሺ݅ሻ = ݊‫ݎܾ݁݉ݑ‬ ‫݂݋‬ ݈݅݃ℎ‫ݐܽ݌ݐ‬ℎ‫ݏ‬ ‫݃݊݅ݏݑ‬ ‫݁ܿݎݑ݋ݏ݁ݎ‬ ‫,ݎ‬
‫ܫ‬௥ = ቄ
0
1
݊௥ሺ݅ሻ <
݊௥ሺ݅ሻ =
ܿ௥
ܿ௥
ሺ20ሻ
ܰ௥ = ෍ ݊௥ ሺ݅ሻ ߶, ‫ܤ‬௥ ෍ ‫ܫ‬௥ሺ݅ሻ ߶⁄
థ
௜ୀଵ
൘
థ
௜ୀଵ
⇒ ߣ௥ =
ܰ௥
ℎሺ1 − ‫ܤ‬௥ሻ
ሺ21ሻ
Where ݊௥, ‫ܤ‬௥ and ߣ௥ are the number of lightpaths using resource r, blocking on resource r and
light path arrival rate on the resource called offered load on resource and h is the average
lightpath holding time.
4. RESULTS AND DISCUSSION
The performance of MRPR algorithm has been evaluated and compared with the performance of
AUR and LLR algorithms. These alogrithms have been computer simulated using GNU Octave
tool and Ubuntu Operating system 14.04 version. We try to find the shortest path from ‘A’ to ‘H’
as shown in Figure (3). In order to demostrate effect of the blocking probability and
reconfiguration probability on the performace of MRPR, AUR and LLR, routing and wavelength
assignment methods under changing load are presented. The reliabilty ratio is fixed at 0.05356,
which means the network has un-reliable routers.
The reconfiguration probability is defined as the probability that a lightpath is re-routed due to a
router or link failure. Since MRPR algorithm distinguishes between the available paths according
to the failure statistics of the routers and links, and lightpath arrival statistics of routers, different
failure arrival rates and variances are used for links and routers. For this purpose, some of the
links/routers are assumed to be unreliable compared to others, and failure arrival rates, λf, for
unreliable and reliable network elements are taken as 1/1500 and 1/1000 respectively in
simulations.
Figure 4: Performance in WI network whe reliablity ratio =0.05356 and wavelength per fiber =3 nos.

48
Figure 5: Performance in WI network when reliabilty ratio=0.05356 and wavelength per fiber=3nos.
In Figure (4) blocking probability as a function of load per wavelength obtained from the
simulations are given. In these simulations, approximately 12% of routers are assumed to be
unreliable. The results show that, from Figure (4) the blocking probabilities for AUR and MRPR
algorithms are close to each other. This is an expected result, since AUR and MRPR are
equivalent to each other, if all failure statistics are assumed to be the same. And also if the link
costs in MRPR are taken as identical, then MRPR will be behave similar to AUR and LLR.
If reconfiguration probability is considered, MRPR algorithm performs 8% better than the AUR
and LLR algorithm, and reconfiguration probabilities increase as the load per wavelength
increases. This is due to the fact that, as the load per wavelength increases, utilization of fibers
and routers increase and the number of lightpaths affected from a failure increases.
Table 1: Reconfiguration due to repacking
No of
channels
Mean failure
inter-arrival
time (µsec)
Mean inter-
arrival time
(µsec)
Mean
holding time
(µsec)
Rp Rf Cost
1/3 1 3 2 0.00156 0.002781 1.100176
2/3 1 3 2 0.01853 0.002934 1.117317
3/3 1 3 2 0.33333 0.003045 1.504077
4/3 1 3 2
0.799
(above TH)
0.003075 INF
In Figure (5) blocking probability and reconfiguration probability as a function of the ratio of
unreliable routers and links are given. As expected, blocking performance of AUR and MRPR
algorithms are close to each other. On the other hand, as the ratio of unreliable elements gets
larger, the reconfiguration probability increases for both of the algorithms. Simulations show that
MRPR algorithm performs approximately 13% better than AUR and 15% better than LLR
algorithms. Table(1) shows the computed values for Bellman Ford algorithm to find the three
shortest-paths that are encircled in Figure(2) using state space approach, Where Rp is Probability
of reconfiguration due to repacking, Rf is Probability of reconfiguration due to failure at node
and TH is Threshold level. As can be seen from the Table(1), for the channel three, the mean

49
failure inter-arrival time is 1.0 µsec. Mean inter-arrival time 3.0 µsec, and mean holding time is
2.0µsec. The Probability of reconfiguration due to repacking is 0.33333, and Probability of
reconfiguration due to failure is 0.003045 and the cost 1.504077. The proposed method to be any
pratical value obviously, the process of taking statistics should not imply prohibitive costs. The
cost is not prohibitive and Probability of reconfiguration is neglible.
In MRPR algorithm reliable paths between souce and destination are considered in the routing
decisions. Since the statistics are used in conjunction with the present state information, it is
naturally expected, that Kalman filter apporach, achieves optimaly better routing performance
compared to earlier adaptive RWA algorithms.
5. CONCLUSION
In this paper, a detailed study of MRPR algorithm has been presented and its performance has
been evaluated in comparision with AUR and LLR algorithms for the RWA problem in all optical
networks. The study has been made considering the probability of reconfiguration due to failure
of the link (i, j) and the probability of reconfiguration due to failure on router j for the lightpath
to be routed and the probability of reconfiguration due to repacking on the link (i j). The
extensive computer simulations have reaveled that the MRPR algorithm is a better choice for
solving the RWA problems. We suggest that further research in this direction is likely to find the
over-head time taken to MRPR alogrithm for RWA problems.
REFERENCES
[1] Stefano Baroni and Polina Bayvel, Wavelength requirements in arbitrarily connected wavelength-
routed optical networks, Lightwave Technology, Journal of 15 (1997), no. 2, 242-251.
[2] Ezhan Karasan and Ender Ayanoglu, Performance of wdm transport networks, Selected Areas in
Communications, IEEE Journal on 16 (1998), no. 7, 1081-1096.
[3] Altan Kocyigit and Semih Bilgen, Statistically predictive optimal wavelength routing, Optical
Networks Magazine (2003).
[4] Altan Kocyigit, and Semih Bilgen, All-optical networking, Turkish Journal of Electrical Engineering
& Computer Sciences 9 (2001), no. 2, 69-122.
[5] Peter S Maybeck, Stochastic models, estimation, and control, vol. 3, Academic press, 1982.
[6] The kalman filter, an introduction to concepts, Autonomous Robot Vehicles, Springer, 1990, pp. 194-
204.
[7] Athanasios Papoulis and S Unnikrishna Pillai, Probability, random variables, and stochastic
processes, Tata McGraw-Hill Education, 2002.
[8] Rajiv Ramaswami, Multiwavelength lightwave networks for computer communication,
Communications Magazine, IEEE 31 (1993), no. 2, 78-88.
[9] Marcelo Yannuzzi, Xavier Masip-Bruin, Sergio Sanchez-Lopez, Eva Marin-Tordera, Josep Sole-
Pareta, and Jordi Domingo-Pascual, Interdomain rwa based on stochastic estimation methods and
adaptive filtering for optical networks., GLOBECOM, 2006.

50
[10] Jay Y Yoo and Subrata Banerjee, Design, analysis, and implementation of wavelength-routed all-
optical networks: routing and wavelength assignment approach, IEEE Communications Surveys,
Broadband Network area (1997).
[11] Y.Donoso G.A Montoya, F.Solano, An Energy-Efficient and Routing Approach for Position,
Estimation using Kalman Filter Techniques in Mobile WSNs, CCC publications, International Journal
of Computer Communications and Control ISSN-1841-9836, 10(4):500-507 August 2015.
[12] Mohana Kumar S, and Jagadeesh S N, “Routing and Wavelength Assignment for Optical Networks
using Link State Space Technique”, in Proceedings of the 6th IETE National Conference on RF &
Wireless, 09-11 May 2013 shimoga Karnataka pp. 116–119.
[13] Mohan Kumar S, Jagadeesh SN, Swaroop “A case study on Markov model for double fault tolerance,
comparing cloud based storage system”, IJARCCE vol 4, Issue 11, November (2015) pp 240-245.
[14] Boudriga, Noureddine. "Optical burst switching protocols for supporting QoS and adaptive routing."
Computer Communications 26.15 (2003): 1804-1812.
[15] SN, Jagadeesha, and Mohan Kumar S. "Routing in all optical networks using recursive state space
technique." Signal & Image Processing: An International Journal (SIPIJ) Vol.7, No.2, (2016) pp 23-
37.
[16] Xiang, Lei, Dingshan Gao, Yu Yu, "Silicon-Based Integrated Comb Filter and Demultiplexer for
Simultaneous WDM Signal Processing." Selected Topics in Quantum Electronics, IEEE Journal of
20.4 (2014): 240-247.
[17] Qin, Haohao, et al. "Power allocation and time-domain artificial noise design for wiretap OFDM with
discrete inputs." Wireless Communications, IEEE Transactions on 12.6 (2013): 2717-2729.
[18] Boudriga, Noureddine. "Optical burst switching protocols for supporting QoS and adaptive routing."
Computer Communications 26.15 (2003): 1804-1812.
[19] Karasan, E., Ayanoglu, E., “Effects of Wavelength Routing and Selection Algorithms on Wavelength
Conversion Gain in WDM Optical Networks”, IEEE/ACM Trans. On Networking, vol. 6, no. 2, pp
186-195, April 1998.
[20] Gerstel, O., Ramaswami, R., “Optical Layer Survivability: A Services Perspective”, IEEE Comm.
Mag., pp. 104-113, March 2000.
[21] Papoulis, A., “Probability, Random Variables, and Stochastic Processes”, McGraw-Hill, 1984.
[22] Karasan, Ezhan, and Ender Ayanoglu. "Effects of wavelength routing and selection algorithms on
wavelength conversion gain in WDM optical networks." IEEE/ACM Transactions on networking 6.2
(1998): 186-196.

51
AUTHORS
Mohan Kumar S. received his Bachelor of Engineering in Computer Science &
Engineering and Master of .Technology, in Networking and Internet Engineering,
from Visvesvaraya Technological University, Belgaum, Karnataka, India
respectively. He is currently pursuing a Doctoral Degree from Visvesvaraya
Technological University, Belgaum, and Karnataka, India. At present he is
working as Assistant Professor, Department of Information Science and
Engineering M.S.Ramaiah Institute of Technology Bangalore. Karnataka. India
(Affiliated to Visvesvaraya Technological University Belgaum). His research
interest includes Communication and networking.
Dr. Jagadeesha S N received his Bachelor of Engineering., in Electronics and
Communication Engineering, from University B. D. T College of Engineering.,
Davangere affiliated to Mysore University, Karnataka, India in 1979, M.E. from
Indian Institute of Science (IISC), Bangalore, India specializing in Electrical
Communication Engineering., in 1987 and Ph.D. in Electronics and Computer
Engineering., from University of Roorkee, Roorkee, India in 1996. He is an IEEE
member. His research interest includes Array Signal Processing, Wireless Sensor
Networks and Mobile Communications. He has published and presented many
papers on Adaptive Array Signal Processing and Direction-of-Arrival estimation.
Currently he is professor in the Department of Electronics and Communication Engineering, PES college of
Engineering. (Affiliated to Visvesvaraya Technological University), Shivamogga, Karnataka, India.

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