Priority-based offline wavelength assignment in OBS networks

1694 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 56, NO. 10, OCTOBER 2008

Priority-Based Offline Wavelength Assignment inOBS Networks

Dong Mei Shan, Kee Chaing Chua, Gurusamy Mohan, and Minh Hoang Phung

Abstract—Optical burst switching (OBS) is a promising tech-nique for wavelength division multiplexing (WDM) networks.In practice, wavelength converters (WCs) are either absent oronly sparsely deployed in WDM networks due to economic andtechnical limitations. Thus, wavelength assignment is expected tobe an important component of OBS networks. In this paper, anoffline wavelength assignment scheme in OBS networks withoutwavelength conversion capability is proposed. The key idea ofthe scheme is to decide the wavelength searching order of eachtraffic connection at edge nodes according to the wavelengthpriorities determined by the calculated burst loss probabilitieson different wavelengths. Simulation results indicate that the pro-posed scheme can reduce the network-wide burst loss probabilitysignificantly compared with other schemes. It is also illustratedthat the performance of the proposed scheme can be furtherenhanced by a larger number of wavelengths per link and areasonable delay bound at edge nodes.

Index Terms—Optical burst switching, wavelength assignment,wavelength conversion.

I. INTRODUCTION

WAVELENGTH division multiplexing (WDM) can sup-port up to hundreds of Gb/s channels over a single

optical fiber. To fully exploit WDM’s high transmission capac-ity, all-optical switching techniques are necessary to bypassthe electrical processing bottleneck at routers. Furthermore,to route Internet Protocol (IP) traffic directly over the WDMoptical layer, future all-optical switching techniques shouldaccommodate the bursty nature of IP traffic [1].

Optical burst switching (OBS) is a promising candidateall-optical switching technique [2] [3]. In an OBS network,IP packets are assembled into data bursts at ingress nodesand disassembled into packets at egress nodes. When a databurst is ready for transmission, a header packet is deliv-ered out-of-band to its egress node on a dedicated controlchannel/wavelength to reserve resources (e.g., wavelengths forburst transmission) at core nodes along the route in advance.After an offset time, the data burst is transmitted withoutwaiting for the reservation feedback from the network. Theinitial value of the offset time should be large enough toensure resource reservation is completed before the burstarrives at any intermediate node. In doing so, OBS realizesall-optical switching by always keeping data bursts in theoptical domain and accommodates bursty traffic by burst-based resource reservation. Furthermore, OBS possesses other

Paper approved by A. Pattavina, the Editor for Switching ArchitecturePerformance of the IEEE Communications Society. Manuscript received June19, 2006; revised January 31, 2007, July 19, 2007, and November 2, 2007.

The authors are with the National University of Singapore, Department ofElectrical and Computer Engineering (e-mail: [email protected]).

Digital Object Identifier 10.1109/TCOMM.2008.060501

advantages compared to optical packet switching (OPS), an-other all-optical switching technique capable of supportingbursty IP traffic. OPS performs packet switching based on in-band signaling. Therefore, techniques that are still immaturesuch as the extraction of headers from optical packets and fastoptical switching are needed in OPS [4]. In OBS, however,because a header packet is separated from its data burst inboth time (for offset time) and wavelength space (for out-of-band signaling), header extraction is avoided and the requiredswitching frequency is lower since the transport unit is larger.

Burst loss probability is an important quality of service(QoS) metric for OBS due to its bufferless characteristic. In theabsence of buffering capability, bursts that cannot acquire theirnecessary resources at a core node will be dropped directly.Though fiber delay lines (FDLs) may be used, they do notreadily support random access and only provide limited delay.To reduce burst loss probability, tunable optical wavelengthconverters (TOWCs/WCs) can be utilized. When two burstscontend for the same wavelength, a WC can convert one ofthem to another free wavelength. However, WCs are expensiveand technologically immature currently. Therefore, they areassumed to be either absent or only sparsely deployed inWDM optical networks [1]. Particularly, OBS networks with-out WCs, i.e., non-convertible OBS networks, are assumed insome recent papers [5-9]. In a non-convertible OBS network,other methods are necessary to reduce burst loss probability;otherwise, it is potentially high at core nodes as determined bya one-server queuing model such as M/M/1/1. These methodsinclude system synchronization, multi-fiber structure, wave-length assignment and so on [5-9]. In this paper, we focus onwavelength assignment. We assume explicit routes have beenset up for all the connections between source-destination nodepairs, whose traffic load information is known in advance.Multi protocol label switching (MPLS) architecture is alsoassumed and a connection is thus known as a label switchedpath (LSP).

The objective of wavelength assignment herein is to orderthe wavelength IDs to form a wavelength searching order(WSO) for each LSP at its ingress node. A burst belongingto an LSP will search wavelengths sequentially according tothe LSP’s WSO at its ingress node for available resources. Assoon as a wavelength is found to be free within a delay bound,the burst will be delayed appropriately and sent out on thatwavelength. The change of WSOs of LSPs (a WSO set) canaffect the burst loss on wavelengths on output links at corenodes. Therefore, by adjusting the WSO set at ingress nodes, itis possible to decrease the network-wide burst loss probability.Wavelength assignment schemes can be realized either online

0090-6778/08$25.00 c© 2008 IEEE

SHAN et al.: PRIORITY-BASED OFFLINE WAVELENGTH ASSIGNMENT IN OBS NETWORKS 1695

or offline [5] [7-9]. In particular, a good offline schemeis desirable since it can be implemented to optimize thesystem performance while neither disrupting ongoing traffictransmissions nor causing the out-of-order arrival problem ategress nodes.

In this paper, an offline wavelength assignment scheme isproposed. Its theoretical basis is a link model extended fromour earlier work in [10], which evaluates burst loss on linksaccording to a WSO set and the traffic load information in thenetwork. The key idea of the scheme is to generate the WSO ofeach LSP according to the wavelength priorities determined bythe calculated burst loss probabilities on different wavelengths.Compared with existing schemes, our proposed scheme isbased on a more accurate link model and can make use ofany possible WSO instead of only a limited set of WSOs.The efficiency of our proposed scheme is verified throughsimulation results.

The paper is organized as follows. To begin with, the relatedworks on wavelength assignment are surveyed in Section II.Following this, the adopted link model is analyzed in SectionIII. The proposed offline wavelength assignment scheme ispresented in Section IV. Simulation results to evaluate theperformance of the proposed wavelength assignment schemeare presented and discussed in section V. Finally, concludingremarks are given in Section VI.

II. WAVELENGTH ASSIGNMENT IN OBS: AN OVERVIEW

Existing wavelength assignment schemes in OBS networkscan be categorized into two groups. The first group consistsof traditional schemes which do not consider the traffic loadinformation in the network. The first fit (FF) scheme [5]belongs to this group. In the FF scheme, wavelengths aresearched in an increasing order according to their IDs. So,all LSPs have the same WSO. In addition, if wavelengthassignment is not limited to setting WSOs for LSPs, traditionalschemes also include the random scheme [5], in which a burstis allocated to a free wavelength randomly and no WSOsneed to be set. In general, being not based on the traffic loadinformation, traditional schemes are inefficient in reducingburst loss probability, as has been verified by simulation resultsin recent papers [5] [9]. Wavelength assignment schemesconsidering the traffic load information belong to the secondgroup called first fit based on traffic engineering (FFTE).Before evaluating existing FFTE schemes, we first introducean optimization model for the wavelength assignment prob-lem. This model shows the problem’s mathematical natureand thereby can be used when existing FFTE schemes areevaluated.

Assume there are L unidirectional links, D LSPs and Wwavelengths per link in the network. Denote the WSOs ofLSPs using a matrix Q with dimensions D × W , whose dth(1 ≤ d ≤ D) row vector qd is the WSO of the dth LSP. WSOqd is a permutation of the integers ranging from one to W.Totally, there are W ! possible permutations which constitutea permutation set P (W ). For example, if W is 2, P (W ) shouldbe {(1 2),(2 1)}. Let C(.) be a function relating the burst lossper wavelength per link to Q. The problem of minimizingthe network-wide burst loss by adjusting the WSO set can be

formulated as a mixed integer programming (MIP) problemas follows.

Minimize

W∑w=1

L∑l=1

C(Q) (1)

Subject to qd ∈ P (W ), d = 1, ..., D.

The objective function is the sum of burst loss per wavelengthper link in the network, which is equivalent to the network-wide burst loss probability for a given traffic load information(similarly, we consider burst loss probability and amountinterchangeable when referring to burst loss performance inthe sequel). The constraint ensures that the WSO of an LSPis a member of P (W ). The number of variables in the modelis the number of elements in Q, i.e., DW .

By the wavelength assignment optimization model, existingFFTE schemes can be divided into heuristic and standardoptimization-based ones according to the methods they adoptto solve the model. A standard optimization-based FFTEscheme is proposed in [9]. In order to simplify the model as acontinuous convex problem, many approximations are made.These approximations decrease the scheme’s performance tothe extent that it performs worse than the following heuristicFFTE schemes.

In the heuristic FFTE scheme proposed in [5], LSPs’ WSOsare decided based on ”inter-node interference”. For a pair ofingress nodes, the inter-node interference is the sum of trafficload of LSPs originating from them and passing at least oneinterference link, which refers to a common link traversed byat least two LSPs each from one of the node pairs. As anexample, assume LSPs d1, d2 and d3 originate from ingressnode g1 and LSPs d4, d5 and d6 from ingress node g2. Amongthese LSPs, LSPs d1 and d4 share link l1 and LSPs d2 andd5 share link l2 along their routes. Therefore, links l1 andl2 are interference links for ingress nodes g1 and g2. Thenthe inter-node interference is the total traffic load of LSPs d1,d2, d4, and d5. Based on the inter-node interference, a startwavelength ID is assigned for each ingress node. An LSPoriginating from one ingress node will adopt the correspondingstart wavelength ID as the first one in its WSO and searchthe wavelengths by their IDs in an increasing and circularway to form its WSO. For example, start wavelength ID wleads to a WSO [w, ..., W, 1, ..., w−1]. Therefore, we can saythat a WSO is set for a node and refer to this scheme as thenode-based FFTE (NFFTE) scheme. The NFFTE scheme hastwo limitations. First, the interference concept is not based onper-wavelength analysis, which does not comply with the opti-mization model. Second, the number of possible WSOs for oneLSP is limited by the number of wavelengths, which is verysmall compared with the number of permutations in P (W ),i.e., W !. This is a serious limitation and may decrease thealgorithm’s performance dramatically. Similarly, an improvedNFFTE scheme considering the number of interference links isproposed in [9]. However, its performance improvement overthe original NFFTE scheme in a network is very small becauseof the inherent limitations of the NFFTE scheme.

Based on the above observations, we propose a heuris-tic FFTE wavelength assignment scheme which avoids theshortcomings of the NFFTE scheme. By adopting a heuristic


1

N

transit stream local stream

link l

Fig. 1. Target link.

method, our proposed scheme is viable with the large numberof variables in the WSO matrix Q. In addition, what followssuggests that there is no close-form objective function, whichmakes it necessary to adopt a heuristic method. Moreover,as an offline one, the scheme will not introduce extra delayat ingress nodes. Any update in the WSOs by it is doneon a long term basis. However, to make the scheme morecomputationally practical, necessary assumptions are madeand methods are used within it to reduce its complexity.The scheme is theoretically based on a link model which ispresented in the next section.

III. LINK MODEL

In this section, we present our proposed link model. Wealso present an iteration method for determining the valuesof unknown variables in the model, which are required forcalculating burst loss on a link.

A. Target Link

Consider unidirectional link l supporting W wavelengthsshown in Fig. 1. As a general case, the node preceding linkl may act as an ingress node and a core node. This node isreferred to as an integrated node in this paper and has appearedin earlier papers [11] [12]. Therefore, link l has two kindsof input traffic, viz., locally generated traffic (local traffic)originating from the integrated node and transit traffic to beforwarded via link l to downstream nodes. Its transit traffic iscarried by N input links. To facilitate discussion, we call thetraffic within one input link as a transit stream and the localtraffic as a local stream. Therefore, link l has N + 1 inputstreams, which are labelled from zero to N: stream n refersto the local stream when n = 0 and a transit stream when1 ≤ n ≤ N . Within stream n (0 ≤ n ≤ N ), there are Mn

LSPs. Amongst these LSPs, the ones within a transit streamare named transit LSPs and those within the local stream localLSPs.

Transit traffic has different stochastic characteristics fromlocal traffic. Local traffic is always assumed to be Poisson.However, transit bursts carried by one wavelength on one inputlink arrive sequentially, that is, the inter-arrival time betweena burst and the immediate burst after it is always greater thanits length. The input traffic of link l can be described usingfollowing notations.

• λw,n,m: traffic rate on wavelength w of the mth (1 ≤ m ≤Mn) transit LSP within transit steam n (1 ≤ n ≤ N )

• λw,n: traffic rate on wavelength w of the nth (1 ≤ n ≤ N )transit stream, i.e.,

∑Mn

m=1 λw,n,m

• λ0,m: traffic rate of the mth (1 ≤ m ≤ M0) local LSP• λ0: traffic rate of the local stream, i.e.,

∑M0m=1 λ0,m

Furthermore, we assume burst length follows exponentialdistribution with an average of 1

μ . Then above mentionedtraffic rates can be mapped into corresponding traffic load byρ = λ

μ .Given the traffic load information of link l, the offered load

of different wavelengths on link l can be represented using anoffered load matrix Sl with dimensions W × (M0 + N). Thewth row vector sl

w of Sl is

slw = [ρw,0,1...ρw,0,m...ρw,0,M0 ρw,1...ρw,n...ρw,N ], (2)

where ρw,0,m is the contribution of local LSP m to the offeredload of wavelength w and ρw,n (1 ≤ n ≤ N) is λw,n

μ .Similarly, the throughput on wavelengths on link l can bedescribed using a throughput matrix Ul with the same sizeas Sl. The wth row vector ul

w of Ul is

ulw = [βw,0,1...βw,0,m...βw,0,M0 βw,1...βw,n...βw,N ], (3)

where βw,0,m is the throughput of local LSP m on wavelengthw on link l and βw,n (1 ≤ n ≤ N) the throughput of transitstream n.

The elements in Sl and Ul can be categorized into localand transit ones which are related to local and transit traffic,respectively. The values of the transit elements in Sl areknown from the traffic load information of link l. However, thevalues of the local elements in Sl must be determined basedon both local LSPs’ traffic load information (i.e., λ0,m with1 ≤ m ≤ M0) and their WSOs, which will be explained in thenext section. So, the unknown variables in Sl and Ul includethe local elements in Sl and all the elements in Ul. Thesevariables’ relationships are reflected in the later proposed linkmodel and their values can be determined using an iterationmethod. Based on their values, the throughput on differentwavelengths is known from Ul and can be used 1) to determinethe input traffic of downstream links, and 2) to determine theburst loss on link l using the following formula, from whichthe network-wide burst loss performance can be determineddirectly.

ρlloss = ρ0 +

W∑w=1

N∑n=1

ρw,n

−W∑

w=1

M0∑m=1

βw,0,m −W∑

w=1

N∑n=1

βw,n (4)

B. Link Model

Given the traffic load information of link l and localLSPs’ WSOs, the objective of our proposed link model is todetermine the relationships amongst unknown variables in Sl

and Ul. Considering that a burst belonging to local traffic canchoose any available wavelength while a burst belonging totransit traffic can only be assigned to its original wavelength,we introduce two sub-models in our proposed link model.The first sub-model is a one-wavelength contention modeldescribing the contention situation on one wavelength. Thesecond one is a multi-wavelength contention model illustratingthe contention process of local traffic in the whole wavelengthdomain.


1) One-wavelength Contention Model: Consider wave-length w, which acts as an output channel on link l and aninput channel on an input link in Fig. 1. Similar to link l, theoutput channel has N + 1 input streams defined in terms ofwavelength w instead of link l. Particularly, stream n refersto the local stream when n = 0 and a transit stream when1 ≤ n ≤ N . The traffic load and rate of the nth (1 ≤ n ≤ N )transit stream are ρw,n and λw,n, respectively. The idle lengthbetween two consecutive bursts within transit stream n isassumed to follow exponential distribution, whose averagevalue Fw,n is

Fw,n =1

λw,n− 1

μ, 1 ≤ n ≤ N. (5)

The traffic load and rate of the local stream are ρw,0 and λw,0,respectively. By Eq. (2), we get

ρw,0 =λw,0

μ=

M0∑m=1

ρw,0,m. (6)

In addition, as local LSPs have Poisson-distributed trafficrates, we assume the traffic of the local stream, which iscontributed by these LSPs, is Poisson.

A (N + 1)-dimensional Markov model can be developedbased on the assumptions made above to describe the con-tention situation on one wavelength. Let xn (1 ≤ n ≤ N )represent the state of the nth input channel: xn = 1 when it isbusy and xn = 0 otherwise. Let y represent the state on theoutput channel: state I denotes that the output channel is freeand state n (0 ≤ n ≤ N ) means that the output channel istransmitting a burst belonging to stream n. Then a state in themodel can be denoted as (x1, ..., xN , y). The transition ratesin the model are expressed using λw,0 and Fw,n (1 ≤ n ≤ N).In this model, however, there are no local balance equations,which means an equation array has to be resolved to determineits steady state probabilities. The following example illustratesthis point. Assume N is 4. Thus, state (1, 1, 0, 0, 1) can convertto and from states (1, 1, 1, 0, 1), (1, 1, 0, 1, 1), (1, 0, 0, 0, 1) and(0, 1, 0, 0, 0, 0), each of which can also convert to and fromother multiple states.

From a computational complexity point of view, solving anequation array to determine the steady state probabilities ofthe one-wavelength contention model should be avoided. It isbecause these probabilities have to be calculated repeatedly inthe later proposed iteration algorithm for determining the val-ues of unknown variables in matrices Sl and Ul. Therefore, weintroduce an approximate model with close-form steady stateprobabilities. The approximate model is a one-dimensionalMarkov model shown in Fig. 2, which only considers the stateson the output channel: state I denotes that the output channelis free and state n means that the output channel is occupiedby a burst from stream n (0 ≤ n ≤ N). The transition rateνw,n from state I to state n is

νw,n =

{λw,0, n = 0;

1Fw,n

= λw,n

1−ρw,n, n > 0.

(7)

Steady state probabilities in the model can be determinedas follows. Let

ϑw,n =νw,n

μ. (8)

0 1 N

lμ

μμ

Fig. 2. One-wavelength contention model.

Let πn be the steady probability of state n and πI of state I ,we have

πn = ϑw,nπI , (9)

πI +N∑

n=0

πn = 1. (10)

Hence,

πI =1

1 +∑N

n=0 ϑw,n

, (11)

πn =ϑw,n

1 +∑N

n=0 ϑw,n

, 0 ≤ n ≤ N. (12)

Since only one wavelength is considered in the model, πn

is in fact the throughput of stream n on the output channel.Therefore,

βw,n = πn, 0 ≤ n ≤ N. (13)

Based on βw,n, the throughput of an LSP on wavelength wcan be determined using

βw,n,m = βw,nρw,n,m

ρw,n, 1 ≤ m ≤ Mn. (14)

This one-wavelength contention model is an approximateone since νw,n (n > 0) defined in Eq. (7) is not accuratewhen there are multiple input streams, among which at leastone stream is a transit one. As an example, consider bursts b1

and b2 overlap with each other and belong to different streams.Particularly, burst b2 is within transit stream n (1 ≤ n ≤ N)and its header arrives when burst b1 is being transmitted by theoutput channel. So, burst b2 must be dropped since it cannot beallocated to the output channel. As a result, in the duration ofburst b2, it is impossible that the output channel is occupiedby a burst from transit stream n, because input channel nis transmitting the dropped burst b2. Therefore, νw,n is zerowhen the output channel becomes free before the end time ofburst b2, which is different from what defined in Eq. (7).

The model is accurate in two cases, namely, when there isonly local traffic and when there is only one transit streamfrom an input channel. In the first case, the model is theM/M/1/1 queuing model. In the second case, the throughput onthe output channel equals the load of transit traffic by Eq. (12),or burst loss is zero. This complies with the fact that burstsarrive in order from an input channel and thus experienceno contention. The model’s accuracy in the second case isimportant, which is explained in Section IV-D.


2) Multi-wavelength Contention Model: Multi-wavelengthcontention model is about the contention process of localtraffic in the whole wavelength domain. For a burst belongingto one local LSP, it has to search the wavelengths sequentiallyfor available resources within a delay bound. As soon as a freewavelength, say w, is found, the burst will be delayed appro-priately and sent onto it. Therefore, the burst cannot contributeto the offered load of the wavelengths after wavelength w inthe LSP’s WSO. This process can be described using followingmodel:

ρqm,1,0,m = ρ0,m, (15)

ρqm,i,0,m = ρqm,i−1,0,m − βqm,i−1,0,m, (16)

i = 2, 3, .., W, m = 1, ..., M0.

The model shows that the contribution of local LSP m tothe offered load of the first wavelength qm,1 in its WSO qm

is always ρ0,m, because all the bursts of LSP m will try to beallocated onto wavelength qm,1 first. However, its contributionto the offered load of wavelength qm,i (i ≥ 2) is only the loadthat cannot be allocated to the previous i − 1 wavelengths inits WSO.

C. Iteration Method

1) Iteration Method Description: The proposed modelshows the relationships among unknown variables includingthe local elements in the offered load matrix Sl and allthe elements within the throughput matrix Ul. Among theseunknown variables, as long as the local elements in matrix Sl

are determined, the values of the elements within matrix Ul

can be deduced by the one-wavelength contention model andEqs. (13) and (14). On the other hand, for the local elementsin Sl, by Eqs. (7), (12), (14) and (16), we get

ρqm,i,0,m = ρqm,i−1,0,m − (17)ρqm,i−1,0,m

1 +∑M0

m′=1 ρqm,i−1,0,m′ +∑N

n=1

ρqm,i−1 ,n

1−ρqm,i−1,n

.

Therefore, the link model is in fact a non-linear equationarray to be solved, with the local elements in matrix Sl asvariables. This equation array is solvable, because the numberof equations is equal to the number of variables and there is noredundant equation. Nevertheless, it cannot be solved directlydue to its non-linear property. In the following, we will adoptan iteration method to get the solution.

The main idea in the iteration method is to update the valuesof the local elements in Sl one by one until a terminatingcondition is satisfied. In the initialization stage, Sl is initializedbased on Eq. (15). In the updating stage, all the local LSPswill in turns update their related elements according to anupdating procedure. During the updating procedure for oneLSP, say m, elements ρqm,2,0,m, ..., ρqm,W ,0,m will be updatedin order based on Eq. (17). After all the local elements havebeen updated, a terminating condition will be checked: if theabsolute value of the maximum relative change of the elementsin Sl is less than a threshold ε, the algorithm stops; otherwise,it continues. What follows is the detailed algorithm, whoseoutput is the offered load matrix Sl.

• Step 1: Initialization1. Sl = 0W×(M0+N )2. ρqm,1,0,m = ρ0,m, m = 1, .., M0

3. Sl,old = Sl

• Step 2: Updating1. for (m = 1, ..., M0)

for(i = 2, ..., W )ρqm,i,0,m = ρqm,i−1,0,m − βqm,i−1,0,m

end forend for

2. if (max∀i,∀j(| sli,j−sl,old

i,j

sli,j

|) ≤ ε)end

elseSl,old = Sl, go to Step 2: 1

end if

2) Discussions on Iteration Method: The above iterationmethod assumes that unknown variables in an offered loadmatrix are the local elements. However, in a complete network,all the elements in an offered load matrix are to be determined.There are two reasons for this. First, an LSP’s traffic loadcarried by one wavelength channel varies along its routebecause of burst loss. Second, integrated nodes may exist inthe network, which inject additional traffic onto a route. Inboth cases, the values of the transit elements in an offeredload matrix are unknown and determined by the through-put matrices of corresponding upstream links. However, athroughput matrix is deduced from an offered load matrix.Therefore, offered load matrices are coupled together and mustbe decided simultaneously.

Nevertheless, the iteration method may take a long timeto converge if it is carried out for all the offered loadmatrices simultaneously, due to the potentially large numberof variables. This difficulty will disappear in an OBS networkwithout integrated nodes, under the simplifying assumptionthat the traffic load of an LSP carried by one wavelengthremains the same along its route. In this case, the offered loadmatrix of each link preceded by an ingress node is only relatedto the link’s local LSPs and therefore can be determinedseparately. After that, based on the throughput matrices onthese links, the input matrices of those links preceded by corenodes can be decided directly. This observation will be usedin our proposed offline wavelength assignment scheme.

IV. PROPOSED OFFLINE WAVELENGTH ASSIGNMENT

SCHEME

The proposed offline wavelength assignment scheme isaimed to determine the WSOs of LSPs to minimize thenetwork-wide burst loss. The assumption made in the schemeis that the traffic load of an LSP carried by one wavelengthremains the same along its route after its source node (theimpact of this assumption on the scheme’s performance isdiscussed in Section V). Three main steps construct thecomplete framework of the scheme, each of which is anindependent algorithm. The three algorithms are a topologyapproximation algorithm, a priority-based FFTE algorithm,and a WSO extending algorithm in the wavelength domain,respectively. Because the second algorithm is the core one,we name our proposed scheme after it as the priority-based


FFTE (PFFTE) scheme. In this section, the details of the threealgorithms are presented and the adoptable link models for thePFFTE scheme are also illustrated and compared.

A. Topology Approximation Algorithm

The aim of the topology approximation algorithm is toreduce the complexity of solving link models in a network asexplained previously. In the algorithm, each integrated nodeis separated into an edge node and a core node connected byone bidirectional link. Particularly, an edge node acts as aningress node and an egress node at the same time. Therefore,the original topology is replaced by an approximate one. Afterthe topology approximation, there are only edge and corenodes in the network. Because of the similarity between theapproximate and the original topologies, a WSO set that canwork in the approximate topology can be expected to stillwork in the original one.

B. Priority-Based FFTE Algorithm

The priority-based FFTE algorithm is developed for an OBSnetwork that only contains edge and core nodes. It generatesthe WSOs of LSPs based on the wavelength priorities deter-mined by the calculated burst loss probabilities on differentwavelengths. Below is the outline of the algorithm, followedby some details related to calculating burst loss probabilitieson wavelengths for an LSP.

In the initialization step, each WSO in the WSO set Q isset with an initial value, say from 1 to W. According to Q,the network-wide burst loss ρloss can be calculated based ona link model. After the initialization step, WSOs of LSPs willbe updated using an updating procedure. Assume the numberof edge nodes is G. In one updating procedure, all the Gedge nodes in the network are set as probing nodes one byone. For a particular probing node, LSPs originating from itare referred to as probing LSPs, whose WSOs are updatedat one time. The WSO updating for the probing LSPs isrealized by probing and comparing the contention situationson different wavelengths for each probing LSP. Concretely,the end-to-end burst loss probabilities on wavelengths of eachprobing LSP are first calculated in the probing process; afterthat, comparison is made: the lower the calculated end-to-end burst loss probability on a wavelength of a probing LSP,the higher the priority of the wavelength for the LSP. Anew WSO of a probing LSP is then set as the decreasingorder of wavelengths regarding their priorities. In doing so, itis expected that more traffic is transmitted as throughput atedge nodes on wavelengths with better contention situationsto experience lower burst loss probability. After the WSOupdating for all the probing LSPs, the network-wide burstloss will be re-calculated. If the new WSO set can lead to alower network-wide burst loss, it will be accepted. Otherwise,WSOs of current probing LSPs are restored to their valuesbefore the updating. The algorithm will stop when the relativechange of the network-wide burst loss after an updatingprocedure/iteration is less than a threshold ε.

In above algorithm, the key point is to compare the relativecontention situations on wavelengths based on the calculated

end-to-end burst loss probabilities of a probing LSP on dif-ferent wavelengths. To make a fair comparison, the LSP’sthroughput on different wavelengths at the probing node, i.e.,its traffic load carried by different wavelengths, should be ofthe same value. This value can be determined by assumingprobing LSPs adopt random wavelength assignment schemesimultaneously. Under this assumption, each burst from theprobing node is allocated to a free wavelength randomly.Therefore, the total throughput of a probing LSP evenly dis-tributes among wavelengths. As an example, consider probingLSP d which has a traffic load of ρd and traverses link lpreceded by the probing node. Its throughput on a wavelengthon link l, denoted as βd, is

βd = βlρd

ρl

1W

, (18)

where ρl and βl are the local traffic load of link l and totalthroughput on link l. Since local traffic is Poisson, βl isdetermined using Erlang B formula with ρl as input.

What follows is the detailed algorithm, whose output isWSO matrix Q.

• Step 1: Initialization1. initialize Q2. calculate ρold

loss

• Step 2: Updating1. for (g = 1, ..., G)

1.1 probing− decide the set of probing LSPs, denoted as Ω,

originating from edge node g− decide the throughput of probing LSPs on

wavelengths− obtain qnew

i ,save qoldi , i ∈ Ω

− update Q using qnewi , i ∈ Ω

− calculate ρnewloss

1.2 acceptance judgement− if (ρnew

loss > ρoldloss)

restore Q using qoldi , i ∈ Ω

elseρold

loss = ρnewloss

end ifend for2. if (ρold

loss−ρnewloss

ρnewloss

≤ ε)end

elsego to Step 2: 1

end if

The priority-based FFTE algorithm is a heuristic methodrealized in an iterative manner. It can terminate or convergeafter a limited number of iterations as proven below.

Claim 1: The priority-based FFTE algorithm terminatesafter a limited number of iterations.

Proof: Assume the network-wide burst loss is xk after thekth iteration. According to the link model, it can be expectedthat xk has a positive lower bound x∗. In fact, as long as thereis input traffic, the burst loss at edge nodes, which is a part ofx∗, is always greater than zero due to local traffic’s Poissonnature. In addition, according to the terminating condition, we


have

0 < ε ≤ xk − xk+1

xk+1, (19)

which indicates xk > xk+1. Therefore, xk where k ≥ 1constructs a monotonically decreasing sequence with a lowerbound. Hence, after a limited number of iterations, thepriority-based FFTE algorithm terminates.

It is worth noting that the convergence point of the priority-based FFTE algorithm is not necessarily optimal. Neverthe-less, later simulation results and related analysis show that theperformance of the priority-based FFTE algorithm is good dueto both its mechanism and the link model adopted by it.

C. WSO Extending Algorithm in the Wavelength Domain

The priority-based FFTE algorithm’s complexity is closelyrelated to the number of wavelengths per link. Assume theaverage number of hops per LSP is H . In one updatingprocedure, link models must be solved at each hop of the DLSPs to update the WSOs of LSPs. Also, link models mustbe solved on all the L links in the network to decide thenetwork-wide burst loss after updating the WSOs of probingLSPs for each of the G edge nodes. Therefore, DH +GL linkmodels must be solved in one updating procedure. However,the number of variables within a link model is proportionalto the number of wavelengths. Therefore, when the numberof wavelengths increases, the complexity of the priority-basedFFTE algorithm ascends.

The WSO extending algorithm is aimed to give an alterna-tive choice to implement the priority-based FFTE algorithmwith a lower complexity when the number of wavelengthsW is large. Let W be a factor of W. A wavelength domainwith W wavelengths is referred to as a compressed domainrelative to the original one. The priority-based FFTE algorithmonly needs to be implemented in the compressed wavelengthdomain to decide a WSO set Q. After that, required WSO setQ can be obtained by mapping Q into the original wavelengthdomain. Concretely, each wavelength ID w in the compresseddomain is mapped as a group of ordered wavelength IDs inthe original wavelength domain. The start wavelength ID ofthe group is equal to (w − 1)W

W+ 1 and the group members

are wavelengths from (w − 1)WW

+ 1 to w WW

. For example, if

W is 9, W can take 3. A WSO of [1 3 2] in the compresseddomain will be mapped as [1 2 3 7 8 9 4 5 6] in the originaldomain. What follows is the detailed algorithm, with WSOmatrix Q as output.

• Step 1: Decide W which satisfies mod(W, W ) = 0• Step 2: Decide Q in the compressed wavelength domain• Step 3: Expand Q into Q

for (d=1...D)for (i = 1...W )

1. w′ = (qd,i − 1) ∗ WW

+ 12. p = (i − 1) ∗ W

W+ 1

3. for (j=0,...,WW

-1)qd,p+j = w′ + j

end forend for

end for

D. Adoptable Link Models in the PFFTE Scheme

The link model proposed in this paper is not the onlycandidate model for the PFFTE scheme. In fact, two kindsof models can be adopted by the PFFTE scheme, viz., single-fiber and multi-fiber link models developed for single-fiber andmulti-fiber OBS networks, respectively. In particular, there isonly one fiber per link in a single-fiber OBS network, whilea link consists of a bundle of fibers in a multi-fiber OBSnetwork. The link model proposed in this paper is a single-fiber one.

In a single-fiber OBS network, the one-wavelength con-tention sub-model can be replaced by other existing modelssuch as M/M/1/1, Engset [13], and the one proposed in [14]to form new link models. These new models differ from ourproposed model in that they either only consider the totaltraffic load of an output channel or assume the input streamsof an output channel have the same traffic load. However, wewill choose our proposed model as the basis of the PFFTEscheme based on the following analysis.

To begin with, according to the observation presented in[10], the relative traffic load among the input streams of anoutput channel has a significant influence on the burst lossperformance on the output channel. As an example, consideran output channel whose total input load is one Erlang from Ntransit input streams. In one extreme case, all the traffic loadconcentrates to one input stream. Since bursts arrive orderlywithin the stream, there is no contention for the output channelamong bursts and thus the burst loss probability is zero. Inanother extreme case, these N streams have the same load, i.e.,1N Erlang, and N is very large. In this case, bursts contendwith each other and the input traffic can be approximatelyconsidered as Poisson. Therefore, burst loss probability is 50%by the M/M/1/1 queueing model. This example implies that itis desirable that the majority of the input traffic of an outputchannel comes from one input channel to form a dominantstream.

On the other hand, the number of input streams of eachoutput channel is fixed, since the routes of LSPs have beendetermined. So, under a particular traffic load, the mechanismof a WSO set affecting the burst loss performance is changingthe relative traffic load among the input streams of outputchannels. If the link model within a FFTE scheme can relatethe relative load difference among the input streams with burstloss performance on an output channel, the scheme’s goodperformance can be expected since the formation of dominantstreams can be encouraged. However, besides our proposedmodel, other models cannot meet this requirement. Hence, wepredict a PFFTE scheme based on our model will have thebest performance compared with other choices. This will beverified in the simulation part.

In a multi-fiber OBS network, there are, say F , fibersper link and, therefore, F channels per wavelength, each onone fiber. In this case, the one-wavelength sub-model canbe M/M/F/F, Engset [13], the one proposed in [14], andthe one we have proposed in [10]. In particular, the modelin [10] is a general case of the one-wavelength sub-modelused in this paper at core nodes. However, due to the pagelimitation, we will not discuss the PFFTE scheme in a multi-


1

72

3

4

5

6

10

8

9

11 12

14

13

Fig. 3. NSFNET network.

21 43

65 87

109 1211

1413 1615

Fig. 4. Torus network.

fiber environment in detail in this paper, but try to clarify andverify the PFFTE scheme in a single-fiber environment.

V. SIMULATION RESULTS

In this section, we use simulations to evaluate the perfor-mance of the PFFTE scheme based on our proposed linkmodel. The PFFTE scheme is mainly compared with theNFFTE scheme proposed in [5] in terms of the network-wideburst loss probability, since the NFFTE scheme is consideredas the most efficient and stable algorithm to date. The mainparameters and assumptions in the simulations are listed asfollows:

• the transmission capacity of each wavelength channel is10Gb/s;

• the locally generated traffic at edge nodes is Poisson;• data burst length follows exponential distribution with an

average of 12.5kB, i.e., 10μs;• the terminating threshold in the PFFTE scheme is 10−3;• the terminating threshold in the iteration method to solve

the link model is 10−6.

Therefore, a stricter terminating threshold has been set to solvethe link model, which is the calculation basis of the PFFTEscheme.

We consider two networks in the simulations, viz., the14-node NSFNET network and a 16-node torus network,which represent irregular and regular network topologies,respectively. In addition, two structures of each topology areconsidered:

• integrated structure: there are only integrated nodes;• edge/core structure: there are only edge and core nodes.

Take the NSFNET network as an example. There are 14 inte-grated nodes with the integrated structure (integrated NSFNETnetwork) while there are 14 core nodes and 14 edge nodes

burs

t los

s pr

obab

ility

edge/core structure: PFFTEintegrated structure: PFFTE

edge/core structure: PFFTE_MM11integrated structure: PFFTE_MM11

edge/core structure: NFFTEintegrated structure: NFFTE

traffic load

10 0

10 -1

10 -2

10

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

-3

Fig. 5. Performance of FFTE schemes in the NSFNET network vs. the trafficload (14 wavelengths per link and zero delay bound at edge nodes).

traffic load0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

edge/core structure: PFFTEintegrated structure: PFFTE

edge/core structure: PFFTE_MM11integrated structure: PFFTE_MM11

edge/core structure: NFFTEintegrated structure: NFFTE

burs

t los

s pr

obab

ility

10 0

10 -1

10 -2

10 -3

10 -4

Fig. 6. Performance of FFTE schemes in the torus network vs. the trafficload (16 wavelengths per link and zero delay bound at edge nodes).

with the edge/core structure (edge/core NSFNET network).Obviously, an edge/core network is the approximate topol-ogy of a corresponding integrated one. Therefore, WSO setsobtained in the edge/core networks can be directly adopted inthe corresponding integrated networks. The signalling protocolis just-enough-time (JET) [2], under which a wavelength isreserved for a burst only in its duration.

In the simulations, we assume there exists one LSP betweeneach source-destination node pair, whose route is decidedusing the shortest path routing algorithm. The traffic load ofLSPs follows uniform distribution in the range of [0 2ρlsp].The traffic load of the network, denoted as ρ in this section,refers to the load of the bottleneck link under the shortest pathrouting scheme, i.e., link 8 → 9 in the NSFNET network andlink 1 → 2 in the torus network. It is equal to ρlsp times thenumber of LSPs traversing the bottleneck link. The load ismeasured in Erlangs per wavelength.

Burst loss probabilities under the PFFTE scheme are com-pared with the ones under the NFFTE scheme against thetraffic load in Figs. 5 to 6. Particularly, Fig. 5 is of the


traffic load0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

integrated NSFNETedge/core NSFNETintegrated torusedge/core torus

perfo

rman

ce a

dvan

tage

(%)

100

90

80

70

60

50

40

30

20

10

0

Fig. 7. Performance advantage of the PFFTE scheme over the NFFTEscheme vs. the traffic load (W wavelengths per link and zero delay bound atedge nodes).

NSFNET network and Fig. 6 of the torus network. The numberof wavelengths per link, i.e., W , in the NSFNET networkis 14 and in the torus network is 16, which is especiallyset to implement the NFFTE scheme (in the sequel, unlessotherwise stated, W takes the same value as in this part).It can be seen from the simulation results that the PFFTEscheme outperforms the NFFTE scheme in both the integratedand the edge/core networks. Quantitatively, the performanceadvantage of the PFFTE scheme over the NFFTE scheme,i.e., (PNFFTE

loss − PPFFTEloss )/PNFFTE

loss , is plotted againstthe traffic load in Fig. 7. It is suggested that the lighterthe traffic load, the larger the performance advantage of thePFFTE scheme over the NFFTE scheme. This phenomenoncan be expected due to the simplifying assumption madein the PFFTE scheme: the traffic load of an LSP carriedby one wavelength remains the same along its route. Suchan assumption is adopted to make it easier to decide theoffered load matrices at core nodes. Under this assumption, alighter traffic load implies more accurate estimations of offeredload matrices and related calculations in the PFFTE scheme,leading to a better performance of the PFFTE scheme.

To explain why the PFFTE scheme performs better thanthe NFFTE scheme, the PFFTE scheme using the M/M/1/1queuing model as the one-wavelength contention sub-model,i.e., the PFFTE MM11 scheme, is also simulated. Simulationresults in Figs. 5 to 6 show that the PFFTE MM11 schemeperforms in between the PFFTE scheme and the NFFTEscheme. This shows that there are two reasons of the good per-formance of the PFFTE scheme. The first reason, as explainedbefore, is that the PFFTE scheme overcomes the mechanismshortcomings of the NFFTE scheme. For instance, a WSO inthe PFFTE scheme can be any permutation of integers rangingfrom 1 to W, while there are only W candidate WSOs in theNFFTE scheme. The second reason is the better accuracy ofthe link model adopted in the PFFTE scheme, which is provenby the fact that the PFFTE scheme has a better performancecompared with the PFFTE MM11 scheme.

burs

t los

s pr

obab

ility

10 0

10 -1

10 -2

delay bound at ingress nodes (μ s)0 25 50 75

PFFTE, ρ=0.8 in NSFNETPFFTE, ρ=0.8 in torusPFFTE, ρ=0.5 in NSFNETPFFTE, ρ=0.5 in torus

NFFTE, ρ=0.8 in NSFNETNFFTE, ρ=0.8 in torusNFFTE, ρ=0.5 in NSFNETNFFTE, ρ=0.5 in torus

Fig. 8. Performance of FFTE schemes in the edge/core networks vs. thedelay bound at edge nodes (W wavelengths per link).

burs

t los

s pr

obab

ility

10 0

10 -1

10 -2

number of wavelengthsW 2W 3W 4W

PFFTE, ρ=0.8 in NSFNETPFFTE, ρ=0.8 in torusPFFTE, ρ=0.5 in NSFNETPFFTE, ρ=0.5 in torus

NFFTE, ρ=0.8 in NSFNETNFFTE, ρ=0.8 in torusNFFTE, ρ=0.5 in NSFNETNFFTE, ρ=0.5 in torus

Fig. 9. Performance of FFTE schemes in the edge/core networks vs. thenumber of wavelengths per link (zero delay bound at edge nodes).

Next we will compare the performances of the PFFTEscheme and the NFFTE scheme with increasing number ofwavelengths and delay bound at edge nodes. Intuitively, theeffect of FFTE schemes is to isolate the LSPs’ traffic loadto different wavelengths. Therefore, the larger the number ofwavelengths, the better the isolation effect. In addition, thedelay bound at edge nodes can also affect the isolation degree.A longer delay bound means that the traffic of one LSP willconcentrate to a few wavelengths and be separated from theother LSPs more fully. So, it is expected that the performanceof FFTE schemes can be enhanced by prolonging the delaybound at edge nodes.

Burst loss probabilities under the PFFTE scheme and theNFFTE scheme are presented against the delay bound at edgenodes in Fig. 8 when the traffic load is 0.5 and 0.8. Similarly,the results when the number of wavelengths per link increasesfrom W to 4W are plotted in Fig. 9. In these cases, when thenumber of wavelengths per link is greater than W , WSO ma-trix Q is deduced from Q obtained in the wavelength domainwith W wavelengths using the WSO extending algorithm. In


TABLE IPERFORMANCE ADVANTAGE OF THE PFFTE SCHEME OVER THE NFFTE SCHEME

number of wavelengths W W 2W 3W 4Wdelay bound 0μs 25μs 50μs 75μs 0μs

NSFNET ρ = 0.5 58% 86% 92% 95% 78% 85% 89%ρ = 0.8 25% 58% 70% 75% 42% 52% 58%

torus ρ = 0.5 67% 86% 92% 95% 80% 85% 88%ρ = 0.8 46% 71% 79% 82% 62% 68% 72%

all these scenarios, only the edge/core structure is considered.Simulation results show that the WSO extending algorithmcan reduce the complexity of the PFFTE scheme efficientlywhile retaining its performance advantage over the NFFTEscheme. More importantly, simulation results also indicatethat burst loss probability decreases under both the PFFTEscheme and the NFFTE scheme in some degree when the delaybound and the number of wavelengths increase. However, theperformance advantage of the PFFTE scheme over the NFFTEscheme given in Table I indicates that the PFFTE scheme canbenefit more compared with the NFFTE scheme.

To explain above phenomenon, we first show that the inputload of an output channel preceded by a core node, saywavelength w on output link l after core node g, tends tobe unchanged with the increasing number of wavelengths anddelay bound under a fixed traffic load measured in Erlangsper wavelength. First, according to the multi-wavelength con-tention model, the contribution of an LSP to the offered load ofa wavelength depends on the position of the wavelength withinthe LSP’s WSO. Concretely, the farther the wavelength to thefirst position of an LSP’s WSO, the less the contribution of theLSP to the offered load of the wavelength. Since wavelengthw is likely to be uniformly located within the WSOs of LSPstraversing link l under either the PFFTE scheme or the NFFTEscheme, its offered load contributed by these LSPs is equalto that when wavelength w is in the middle of these LSPs’WSOs, which is the sum of average throughput per wavelengthof these LSPs on the input links of node g. Second, since theburst loss probability of each LSP at its ingress node is usuallysmall and the traffic load per wavelength in the network isfixed, the average throughput per wavelength of an LSP ona particular link does not change much with the increasingnumber of wavelengths and delay bound.

With the input load of an output channel not changing much,relative load difference among its input streams determines theburst loss on it, according to the discussion in Section IV-D.The NFFTE scheme, however, does not consider the trafficload of individual streams of an output channel, leading toits inefficiency in controlling the relative input stream load.Hence, the performance of the NFFTE scheme is mainlydetermined by the input load of each output channel andtherefore alters slightly. On the contrary, the PFFTE schemeis based on stream load analysis. So, it is potentially capableof benefiting more from the increases in the number ofwavelengths and delay bound, which provide more flexibilityin controlling the relative input stream load.

VI. CONCLUSIONS

This paper has studied the wavelength assignment problemin non-convertible OBS networks. An analytical OBS link

model that can evaluate the impact of a WSO set on the burstloss performance has been presented. Based on this model,an offline wavelength assignment scheme, i.e., the PFFTEscheme, has been proposed. The convergence of this schemehas been proven theoretically.

We have conducted computer simulations in regular andirregular networks. Compared with other schemes, the PFFTEscheme can significantly reduce the network-wide burst lossprobability. We also have used simulations to analyze thereasons of this performance improvement. Simulation resultsindicate that it is due to both the mechanism and the linkmodel adopted by the PFFTE scheme. Furthermore, simulationresults illustrate that the performance of PFFTE scheme canbe enhanced by a larger number of wavelengths per link anda reasonable delay bound at edge nodes.

REFERENCES

[1] C. S. R. Murthy and G. Mohan, WDM Optical Networks: Concepts,Design and Algorithms. Prentice Hall, 2002.

[2] C. Qiao and M. Yoo, “Optical burst switching (OBS) - a new paradigmfor an optical Internet,” J. High Speed Networks, vol. 8, pp. 69-84, Oct.1999.

[3] Y. Xiong, M. Vandenhoute, and H. Cankaya, “Control architecture in op-tical burst-switched WDM networks,” IEEE J. Select. Areas Commun.,vol. 18, pp. 1838-1851, Oct. 2000.

[4] K. C. Chua, G. Mohan, Y. Liu, and M. H. Phung, Quality of Service inOptical Burst Switched Networks. Springer, 2007.

[5] T. Jing and G. N. Rouskas, “Wavelength selection in OBS networksusing traffic engineering and priority-based concepts,” IEEE J. Select.Areas Commun., vol. 23, pp. 1658-1669, Aug. 2005.

[6] J. Ramamirtham and J. Turner, “Time sliced optical burst switching,”in Proc. IEEE INFOCOM 2003, pp. 2030- 2038, Mar. 2003.

[7] X. Wang, H. Morikawa, and T. Aoyama, “Priority-based wavelengthassignment algorithm for burst switched WDM optical networks,” IEICETrans. Commun., pp. 1508-1514, May 2003.

[8] X. Wang, H. Morikawa, and T. Aoyama, “Priority-based wavelengthassignment for burst photonic networks with limited wavelength con-version,” in Proc. IEEE OFC 2002, pp. 765-767, Apr. 2002.

[9] D. M. Shan, G. Mohan, and K. C. Chua, “Offline wavelength assignmentin labelled optical burst switched networks,” in Proc. IEEE HPSR 2005,pp. 467-471, May 2005.

[10] M. H. Phung, D. Shan, K. C. Chua, and G. Mohan, “Performanceanalysis of a bufferless OBS node considering the streamline effect,”IEEE Commun. Lett., vol. 10, pp. 293-295, Apr. 2006.

[11] Y. Sun, T. Hashiguchi, and V. Minh, “Design and implementation ofburst switching nodes for WDM optical networks,” in Proc. SPIE APOC2004, pp. 464-472, Nov. 2004.

[12] X. Yu, J. Li, X Cao, Y. Chen, and C. Qiao, “Traffic statistics andperformance evaluation in optical burst switched networks,” IEEE/OSAJ. Lightwave Technol., vol. 22, pp. 2722-2738, Dec. 2004.

[13] H. Akimaru and K. Kawashima, Teletraffic–Theory and Applicatoin.Springer, 1999.

[14] M. Zukerman, E. Wong, Z. Rosberg, G. Lee, and H.L. Vu, “On teletrafficapplications to OBS,” IEEE Commun. Lett., vol.8, pp. 116-118, Feb.2004.


Shan Dong Mei received her B. Eng. degree andM. Eng. from Beijing University of Aeronauticsand Astronautics (BUAA), P. R. China in 1998 and2001, respectively. She is currently working towardsher Ph. D. degree in the Department of Electricaland Computer Engineering at National Universityof Singapore. Her current research interests focuson WDM optical networks.

Kee Chaing (KC) Chua received a Ph.D. degree inElectrical Engineering from the University of Auck-land, New Zealand in 1990. He joined the NationalUniversity of Singapore (NUS) as a Lecturer in1990 and is now a Professor in the Department ofElectrical & Computer Engineering. He served asthe Faculty of Engineering’s Vice Dean for Researchfrom June 2003 to March 2006. From 1995 to2000, he was seconded to the Center for WirelessCommunications (now part of the Institute for In-focomm Research), a national telecommunication

R&D centre funded by the Singapore Agency for Science, Technology andResearch as its Deputy Director. From 2001 to 2003, he was on leave ofabsence from NUS to work at Siemens Singapore where he was the foundinghead of the Mobile Core R&D Department funded by Siemens’ ICM Group.Since March 2006, he has been seconded to the National Research Foundationas a Director.

Dr. Chua has carried out research in various areas of communication net-works and has published more than 200 papers in these areas in internationalrefereed journals and conferences. His current research interests are in wirelessnetworks (in particular wireless sensor networks) and optical burst switchednetworks. He has also been an active member of the Institute of Electrical& Electronics Engineers (IEEE), Inc., and is a recipient of an IEEE 3rdMillennium medal.

Mohan Gurusamy (M’00-SM’07) received thePh.D. degree in Computer Science and Engineeringfrom the Indian Institute of Technology, Madras in2000. He joined the National University of Sin-gapore in June 2000, where he is currently anAssociate Professor in the Department of Electricaland Computer Engineering. He has held a visit-ing position at Iowa State University, USA, fromJanuary-June 1999. He served as the lead guesteditor for two special issues on Optical NetworkingTestbeds of the IEEE Communications Magazine

(OCS), August 2005 and November 2005. He was the organizer and leadchair of IEEE/CreateNet GOSP workshop collocated with IEEE/CreateNetBroadnets conference, October 2005 and October 2006, USA. His researchinterests are in the areas of high speed multi-wavelength optical circuit andburst switching networks, wireless sensor networks, and grid networks. Hehas over 100 research publications to his credit and co-authored two books inthe area of optical networks. Dr. Gurusamy has been a member of the IEEEsince 2000 and senior member since 2007.

Phung Minh Hoang received his B.Eng degreein Telecommunications (first-class honors) from theUniversity of Sydney, Australia in 2000 and his PhDin Electrical Engineering from National Universityof Singapore in 2006. He is currently a ResearchFellow in the Department of Electrical and Com-puter Engineering, NUS. His research interests focuson networking issues in WDM optical networks andaccess networks.

Priority-based offline wavelength assignment in OBS networks

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