462 journal of lightwave technology, vol. 27, no. 5, march 1

462 JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 27, NO. 5, MARCH 1, 2009

QoS-Aware Wavelength Assignment With BER andLatency Constraints for All-Optical Networks

Jun He, Member, IEEE, Maïté Brandt-Pearce, Senior Member, IEEE, and Suresh Subramaniam, Senior Member, IEEE

Abstract—Physical impairments originating from optical fibercomponents and intermediate switching nodes can be the domi-nant reason calls are blocked in wide-area all-optical wavelengthdivision multiplexing (WDM) networks. When a centralizednetwork controller is used, estimating the impact of the physicalimpairments on the quality of a lightpath before provisioning itcan cause a significant delay. In this paper, quality of service awarewavelength assignment algorithms are proposed that considerboth bit-error rate (BER) and latency constraints. A novel wave-length assignment technique called wavelength ordering is shownvia simulation to reduce the call blocking probability resultingfrom both physical impairments and excessive processing delaycaused by channel BER estimation.

Index Terms—Crosstalk, optical fiber communication, opticalswitches, routing and wavelength assignment (RWA), transmissionimpairments, wavelength division multiplexing (WDM).

I. INTRODUCTION

A LL-OPTICAL wavelength division multiplexed (WDM)networks have been proposed as a promising solution to

satisfy society’s dramatically increasing network throughputdemands. In today’s transport networks, electronic switches re-quiring optical-electro-optical (OEO) conversion have becomecomplex and costly in systems operating at several tens ofGigabits per second. They become a bottleneck when a networkmust sustain a system-wide capacity of several tens of terabitsper second, making it necessary to replace them with all-opticalswitches where no electric conversion is used [1]. As discussedin [1]–[4], deploying networks utilizing all-optical switches ispromising yet also challenging as many novel problems must beanticipated. One difficulty in designing all-optical networks isdetermining how to best assign lightpaths (LPs) to call requestssuch that the impact of physical-layer impairments and delay

Manuscript received March 25, 2008; revised July 24, 2008. Current versionpublished April 17, 2009 This paper was presented in part at the IEEE Op-tical Fiber Conference,Anaheim, CA,March 2006 and the IEEE InternationalConference on CommunicationsGlasgow, U.K., June 2007. This work was sup-ported in part by the National Science Foundation under Grant CNS-0520060and Grant CNS-0519911.

J. He was with the Charles L. Brown Department of Electrical and Com-puter Engineering, University of Virginia, Charlottesville, VA 22904 USA. Heis now with the Department of Computer Science at Texas State University,San Marcos, TX 78666 USA (e-mail: [email protected]).

M. Brandt-Pearce are with the Charles L. Brown Department of Electricaland Computer Engineering, University of Virginia, Charlottesville, VA 22904USA (e-mail: [email protected]).

S. Subramaniam is with the Department of Electrical and Computer Engi-neering, George Washington University, Washington, DC 20052 USA (e-mail:[email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JLT.2008.2004944

is minimized. In this paper we describe and analyze wave-length assignment (WA) algorithms that outperform traditionalmethods when bit-error rate (BER) and latency constraints areenforced.

In an all-optical network, a data signal is transmitted by firstestablishing a LP, i.e., a route from the source to the destinationconsisting of one or more fiber links on a chosen wavelength.1The data signal propagating through a large network encountersphysical impairments such as amplified spontaneous emission(ASE) noise from erbium-doped fiber amplifiers (EDFAs) andcrosstalk from power leaks in optical crossconnects (OXCs)and imperfect WDM demultiplexing. These interferenceslimit system performance as networks expand and wavelengthdensity increases [5], [6]. Physical impairments may cause thequality of the optical signal to degrade and become so poorthat its BER is unacceptably high. Moreover, the inappropriateassignment of new LPs may cause unacceptable degradation onexisting communications.

Conventional studies on routing and wavelength assignment(RWA) have proposed many algorithms for establishing LPswithout considering any physical impairments [7], [8]. Thesetechniques are evaluated by using the blocking probability asa performance metric. A blocking event, called wavelengthblocking, occurs when a LP cannot be set up due to shortageof a free route or a jointly free wavelength along the route.A call/request can also be blocked if the chosen LP hasunsatisfactory BER, called BER blocking, or if the latency in-curred in processing the call exceeds a given constraint, calledlatency blocking. In this paper the term quality-of-service(QoS) blocking, denotes BER blocking and latency blockingcombined.

In the last few years, RWA techniques that consider thequality of transmission (QoT), as measured by the BER, andincorporate both BER blocking and wavelength blocking havebeen the subject of intense research [6], [9]–[19]. We refer toas QoT-guaranteed those algorithms that perform conventionalRWA yet only allow the selected LP to be established if theQoT requirement is met, thereby assuring the QoT of the LP(inducing a much higher blocking rate than the conventionaltechnique that accepts calls even when their QoT is poor)[6]. More sophisticated techniques are truly QoT-aware andselect routes and/or wavelengths based on some criterion ofperformance.

Many researchers consider routing as the main method toreduce the blocking probability and underestimate the powerof WAs [13], [15]–[19]. However, QoS blocking dependssignificantly on the choice of WA algorithm used [6], [9].For instance, it is well-known that wavelength blocking when

1No wavelength conversion is considered as these devices are still in experi-mental phases of development.

0733-8724/$25.00 © 2009 IEEE

Authorized licensed use limited to: The George Washington University. Downloaded on June 8, 2009 at 16:25 from IEEE Xplore. Restrictions apply.

HE et al.: QoS-AWARE WAVELENGTH ASSIGNMENT 463

using the random-pick (RP) WA algorithm, choosing randomlyamongst the available wavelengths, is worse than that whenthe first-fit (FF) WA algorithm, choosing the free wavelengthwith the lowest index, is selected [8]. However, RP WA isa QoT-friendly algorithm because it tends to geographicallyspread wavelength use across the network such that crosstalkeffects are not likely to be severe [6]. Therefore, the overallblocking probability for RP WA may be better than that for FFWA [9], [12], [14]. In this paper we only consider fixed shortestpath routing (SP) and fixed alternate routing (ALT) algorithmsand focus on showing the ability of WA algorithms to reduceQoS-blocking in all-optical networks. All WA techniquesproposed can be applied to other more sophisticated routingalgorithms.

In the QoT-aware WA algorithm proposed in [9] availableLPs are tested until one with a sufficiently low BER is found,and if none exists the call is blocked. In [10], QoT-aware adap-tive RWA algorithms are proposed that incorporate BER infor-mation to yield better performance in terms of average BER andfairness among network users. In [11], the authors propose sev-eral methods to combat four-wave mixing (FWM) impairment.In [12] and [14], we propose two wavelength assignment algo-rithms that only require local traffic information and QoT con-ditions and are therefore suitable for networks relying on dis-tributed controllers.

QoT-aware RWA algorithms are inevitably more complexthan their conventional counterparts due to exhaustive searchand BER estimation. The estimation of QoT is typically notonly performed for each incoming request, but also for each LPthat the proposed connection might disrupt. It is possible that anew LP has an acceptable QoT but provokes so much crosstalkin the network that the QoT for other LPs (interacting LPs)drops below threshold [9]. Moreover, the algorithm has to com-pute the BER on candidate LPs in real-time before accepting acall because the physical impairments are network state depen-dent. The presence or absence of other co-propagating LPs, i.e.,the instantaneous network state, affects the number of crosstalkterms, the saturation of the amplifier gains, and the ASE noise inthe EDFAs. Algorithms that are too complex add unacceptabledelays in call-setup time, making them unsuitable for applica-tion in wide-area networks.

The merit of each WA algorithm is evaluated by its perfor-mance as measured by the blocking probability; complexity isonly penalized by the negative impact it has on the network per-formance (measured by the latency blocking rate) resulting froman excessively long queue or call-setup time. To our knowledge,this is the first study to include both BER and latency thresh-olds when evaluating RWA algorithms. Because both these cru-cial measures of quality are tested, we refer to our algorithmsas QoS-aware. Latency is an important performance measure toservice providers because long setup delay increases the timecost and decreases network utilization. In future networks, la-tency-constrained applications requiring on-demand fast setupof circuits such as large file transfers or video-conferencing areanticipated. For architectures that include an all-optical networkas a high-speed parallel alternative to a conventional network, afixed time-out on the call setup for the WDM network is neededto determine when the service must be contented to use theslower connection. Another example of a call that must be han-dled especially rapidly is dynamic restoration after a failure,

wherein backup paths are not reserved but are discovered dy-namically when needed. The restoration time has to be less than50 ms after a failure has been detected in SONET/SDH networks[20].

We propose several WA algorithms based on QoS require-ments and compare their performance through simulation ex-periments, as was done for QoT-constrained WA in [6], [9]. Oneof the WA algorithms considered uses a promising new heuristicoffline wavelength ordering technique. Section II describes thenetwork system considered, including our models for calcu-lating the latency and BER of the desired LP. In Section III, wepresent the QoS-aware WA algorithms to be compared. We eval-uate the performance of QoS-aware WA algorithms and showthe advantage of wavelength ordering in Section IV. The per-formance of algorithms is measured by total call blocking prob-ability (BP) with both BER and latency constraints. Conclusionsare drawn in Section V.

II. SYSTEM DESCRIPTION

In this section, we first describe the network architecture em-ployed, and then present our model and assumptions for calcu-lating the latency and estimating the BER of the network. Weconsider networks with bidirectional links, each of which sup-ports wavelengths in each direction.

A. Network Architecture

Connection management schemes for WDM networks can beclassified as centralized or distributed [4], [21]. In this work,only centralized circuit-switched WDM networks, a type of con-nection-oriented network, are considered. The reason is that thedelay induced in the admission control for distributed networksdepends highly on the network topology and signaling controlprotocols. Moreover, our most recent research [22] has shownthat the results of QoS-blocking for distributed networks is sim-ilar to the results for centrally controlled networks. Fig. 1 showsa simplified node architecture, including the data-plane and thecontrol plane. Via the control plane the user sends a messagerequesting a communication channel, i.e., a LP connecting onenode in the network to another. The controller checks the routingtable and assigns an appropriate route and wavelength to eachaccepted request. It then sends control messages to all nodesto configure the switches appropriately. Lastly, the controllersends a reply message back to the user, informing it of its as-signed LP.

On the data plane the data sent from the user enter the switchfabric by the optical add/drop multiplexer (OADM). The switchfabric directs the demultiplexed pre-amplified input signalsfrom other network nodes and the new signals from the OADMto the appropriate output ports. The switch fabric could beeither a single multiwavelength large-scale switch, or a set ofsmall-scale single-wavelength switches. More details can befound in [23, Ch. 3.7]. After traversing the switch fabric, signalsnot already at their destinations are amplified and transmittedout through multiplexers to output links. Otherwise, the signalsare switched to the drop ports of the OADM and are receivedby the desired users [21], [24]. The functions of the OADM andswitching fabric are typically integrated within the switch, yetare drawn separately in Fig. 1 for clarity.



Fig. 1. All-optical network node architecture including control plane and data plane. � represents the � wavelength.

The main purpose of the control plane is to reserve net-work resources and program the switching fabric prior to datatransfer. The LPs are reserved in a circuit setup phase andreleased in a circuit tear-down phase. In centralized networks,the controller allocates the resources through the entire LP foreach request during the connection admission control (CAC)procedure. Requests arrive at the controller’s queue and areallocated resources using a first-come, first-served (FCFS)policy. We assume the queue has infinite size yet users arewilling to wait only a limited time for a reply message fromthe controller. The control network can be in-band, where thecontrol messages share the same network as the user data,or out-of-band, where a separate network is maintained formanagement and control message.2 In this paper, we assumethe control channel is out-of-band to avoid the additionalinterference and crosstalk it would otherwise generate.

A critical part of the controller’s function is to assign a LP,i.e., a route and a wavelength, to each call request if possible.The structure of a centralized controller is simplified and showninside the control plane in Fig. 1. In general, a route is deter-mined for the connection by consulting routing tables, whichcan be created dynamically or statically. In this paper, twofixed routing algorithms are considered. In SP, the path with theshortest fiber length is selected. In ALT, one primary path (theSP) and one alternate path (the second shortest) are considered.Because the emphasis of this paper is on the performance ofWA algorithms, more complex dynamic routing is not explored.The reader interested in QoS-aware routing is referred to [15],[25].

Call requests are assumed to arrive at the network nodes ac-cording to a Poisson process with mean arrival rate and calldurations follow an exponential distribution with mean value

. Thus, the offered load per node in the network isErlangs, which is assumed for notational simplicity to be equalfor all nodes. Based on the additive property of Poisson pro-cesses, the total call arrival rate offered to the controller’s queueis , where is the total number of network nodes.

B. Setup Latency

In delay-sensitive networks, the delay incurred during theCAC procedure, labeled , can be unacceptably long. We as-sume a timeout mechanism with a user-adjustable delay bound

2Out of Band Management. [Online]. Available: http://www.mrv.com/li-brary/library.php?ctl=MRV-AN-SFOOBM

, which depends on the application. The controller takesthe call at the head of the queue and iterates through candidateLPs until it has found one satisfying the BER constraint (if oneexists), for as long as this latency constraint is not violated.

The total call admission time for call request can be writtenas the sum of two delays, a queuing delay and a processing delay,

(1)

The queueing delay , which is the time request mustwait in the queue, depends on the sum of the processing delayof previous requests in the queue. The processing delayincludes the time required to check the routing table and the timeto find a viable wavelength. The time required for calculating theset of free wavelengths is denoted , and the time needed forchecking if the BER of a candidate or interacting LP is higherthan the BER threshold is denoted . When using the fixedalternate routing algorithm, the time to find all free wavelengthsin primary and alternate routes is . The time to estimatethe BER of a call depends directly on the network traffic and theinstantaneous network state, and also indirectly on the numberof hops in the LP and the severity of the physical impairments.The delay in processing request is calculated as

(2)

as long as the delay constraint is not exceeded, where is equalto 1 if SP is applied or 2 if ALT is applied. denotes thenumber of trials before the processor finds a viable (BER lessthan threshold) LP or finishes checking all candidate LPs. As-suming there are existing LPs that interact (share a link ornode) with LP , is equal to if every interacting LP istested for BER compliance. If one interacting LP fails the com-pliance test, the process is terminated for the th candidate LP,and is then the number of interacting LPs tested before thefailure occurs. The moment the delay exceeds , the call isinstantly dropped.

Fig. 2 shows the various delays as a timeline. When a requestis sent by a network node to the centralized controller, trans-

mission delay (the duration of the request packet) and propaga-tion delay (from the node to the controller) are incurred beforethe request goes into the controller’s queue. Networks that ig-nore physical impairments suffer primarily from transmissiondelay and propagation delay in their CAC process [26]; the



Fig. 2. Timeline of a call admission procedure for request � in centralizedWDM networks if � �� .

queueing and processing delays are small if no complex com-putation is performed. The WA algorithms considered here im-pose a significant processing delay because of the time neededto estimate the BER. Since the transmission and propagation de-lays are expected to be small in comparison to , they areignored in this paper so as to simplify the derivation. Thus, thetotal setup latency for call , , is estimated as just the sumof the processing delay and queueing delay if within the timeoutthreshold.

C. Physical Impairments Model

The optical impairments considered in this study are insertionlosses of components, node crosstalk, and noise, including ASEnoise from the EDFAs, shot noise, and thermal noise at the re-ceivers. Amongst these the crosstalk dominates [6]. The EDFAsare modeled as automatic gain controlled (AGC) as in [9], [23].Fiber dispersion and nonlinearity, which could be incorporatedusing models available in the literature, are not included herefor simplicity.

For our analysis, we assume that all crosstalk terms areincoherent and modeled as in [6] and [23]. Crosstalk originatesmainly from two sources at the switching nodes, as shown inFig. 3. In the switching fabric, a small fraction of one signal issent to the wrong output port at the same wavelength, givingrise to what we call switching fabric crosstalk. In Fig. 3, this isshown by a part of ’s signal becoming the switching fabriccrosstalk when and meet during the wavelengthswitching. The other source of crosstalk is the demultiplexer,in which a portion of the power from adjacent wavelengthscannot be entirely filtered out, which we then call adjacent-portcrosstalk. In the figure, this occurs when a proportion of

’s signal becomes crosstalk to as and aremultiplexed together in the output. This crosstalk power levelis wavelength dependent, with crosstalk from immediatelyadjacent wavelengths more powerful than crosstalk from thoseseparated by one or more wavelengths. We model the powerof the demultiplexer crosstalk as exponentially decaying withspectral separation making the crosstalk contribution fromwidely separated wavelengths negligible. The exact decayfactor depends on the modulation type and device specifi-cations. The switching fabric crosstalk level depends on thedevice type, which typically ranges from 25 dB to 35 dBfor non-mechanical switches and below 40 dB for MEMSswitches. The level of the switching fabric crosstalk and adja-cent-port crosstalk powers normalized to the main signal powerare denoted and , respectively.

Assuming binary on-off keying (OOK) modulation, the BERof the signal can be approximated using the statistics of the re-ceived signal after photodetection, filtering, and sampling for

Fig. 3. Example illustrating the sources of imperfect WDM demultiplexing ad-jacent-port crosstalk and switching fabric crosstalk. A WRS is a wavelength-routed switch; CH refers to a channel.

both the “0” and “1” bits. Denote by and the mean valueand the standard deviation of receiver samples for a “0” bit, andby and the mean value and the standard deviation of re-ceiver samples for a “1” bit, respectively. Normally, both and

are small; dominates since it includes beat terms as wellas noise. It can be written as the sum of variances from noiseand crosstalk components, which are assumed to be indepen-dent [27]

(3)

where , , and are variances due to ASE noise,thermal noise, and shot noise, respectively, computed as in [28,eqs. (6.1.17), (4.4.5), and (4.4.8) ]. The crosstalk variance is cal-culated as [6]

(4)

where is the receiver responsivity, is the received signalpower, and is the sum of all crosstalk terms’ powers at thereceiver.

Under a Gaussian noise assumption, the BER can be approx-imated by , where the Q factor is givenby [28]

When a LP is established, the BER for LPs that share one ormore OXCs must be re-estimated to account for crosstalk com-ponents that are injected or removed, changing the value ofdirectly through and affecting , , and via the signaldependent EDFA gain. Because of the gain dependence of theAGC EDFA model on the total received power (due to powersaturation), the table look-up technique for estimating the BERproposed in [10], [14] cannot be utilized here and all noises haveto be calculated online.

III. WAVELENGTH ASSIGNMENT ALGORITHMS

In this section, we first demonstrate how the QoS require-ments are applied to well known FF and RP WA algorithms.Then the wavelength ordering technique is presented and a newwavelength assignment algorithm, FF with wavelength ordering(FFwO) is proposed.



Fig. 4. Flowchart of QoS-aware WA algorithms using SP routing incorporatingboth BER and latency thresholds.

A. QoS-Aware and QoS-Guaranteed WAs

We denote as QoS-aware WA algorithms that find LPs thatsatisfy the BER and latency constraints (for the new call andevery already-established LP) by a successive process of tryingavailable wavelengths in the routes found by SP or ALT routingalgorithms. Algorithms not identified as QoS-aware test a singlewavelength in the route found by SP or in the primary route andthe alternate route (if the first trial in the primary route fails)found by ALT for QoS compliance (QoS-guaranteed) and thenquit.

The QoS-aware approach used here is similar to existingQoT-aware WA algorithms [9] except that we consider SPand ALT routing algorithms and add a timeout thresholdto guarantee that the latency of served calls is less than the

threshold. Therefore, QoS blocking includes both BERblocking and latency blocking. BER blocking happens if noavailable LP has a BER value lower than the threshold value(or cannot preserve a sufficiently low BER value for the otherexisting LPs). The flowchart in Fig. 4 illustrates the QoS-awareWA approach using the SP routing algorithm. The centralizedcontroller takes the first call in line from the queue, assignsit a route (from the routing table) and then starts an iterativeprocess to select a wavelength. In this work, the order inwhich wavelengths are tried (for both the QoS-aware and theQoS-guaranteed WAs) can either be RP, FF, or FFwO. Atany point in the CAC processing, if the timeout constraint isviolated, the call is dropped. The WA algorithm uses the BERestimator to determine if a viable wavelength is available thatsatisfies the BER constraint. If a LP is found within the allottedtime, the call is accepted; otherwise, the connection request isrejected.

Note that the algorithms considered here take the first LPthat yields acceptable performance, thus risking the acceptanceof connections with performance close to the BER threshold.We do not consider a Q factor maximizing algorithm (suchas “highest-Q” in [10]) that takes the best performing optionwithin the timeout limit. First, this greedy algorithm wouldtake a longer time performing optimization and would thusleave less time for calls in the queue to perform their LPsearch, forcing more time-out blocking. Second, maximizingthe Q factor does not diminish blocking probabilities, evenwithout a timeout constraint [13], [29], because the increase inwavelength blocking seldom compensates for the decrease inBER blocking.

B. Wavelength Ordering

We seek WA algorithms that are able to reduce crosstalk innetworks for which physical impairments dominate. Adjacentchannel interference can be the limiting degradation in WDMnetworks with dense wavelength packing because demulti-plexers are unable to completely isolate channels. For suchnetworks, to improve performance the WA algorithm mustattempt to use wavelengths that are as spectrally separated aspossible to avoid adjacent-port crosstalk. Simultaneously, theWA should use as few wavelengths as possible to minimizesubsequent wavelength blocking, which the FF WA algorithmexcels at. We propose to use a conventional FF WA techniqueexcept that the wavelengths are pre-ordered so as to minimizethe adjacent wavelength crosstalk effect. In this paper we referto as a channel the sequential order of resources to be accessed.We reserve the term wavelength to refer to the actual spectralband utilized. The WA module then picks the first channelavailable from the list of unused channels, and this channelcorresponds to a particular wavelength that depends on theordering. This idea is similar to the algorithm developed in[11] where WA using ordering is used to reduce four-wavemixing crosstalk that can become the dominant degradation inlong-haul WDM systems.

Wavelength ordering can be done offline (static pre-ordering)or online (adaptive ordering). In this paper we explore the useof a static offline technique. On-line ordering techniques mighttry to minimize the crosstalk for the current network state byfinding all available wavelengths on a route and picking theone that is furthest away spectrally from all used wavelengths.In [12], [14], several dynamic, network state-dependent onlinewavelength ordering algorithms are proposed and discussed.The problem with online ordering is that it destroys the sequencein which the channels are used and with it the property of FFthat tries to pack the traffic in as few channels as possible [8].Sophisticated wavelength spectrum designs can successfully re-move some of the crosstalk in the network, but they have to oc-cupy more disparate wavelengths than the FF wavelength as-signment. They can provide an advantage in cases of intensephysical impairments where the BER-blocking dominates overwavelength-blocking. In networks with dynamic traffic, periodsof negligible physical impairments would experience a higherblocking probability due to the wavelength dispersal resultingfrom state-dependent online ordering. We have found the dif-ference in total blocking probability between dynamic onlineand offline ordering to be minor [12], [14]. Because we wish tominimize delay, in this paper we only consider offline ordering.

The offline ordering algorithm is done once, before thenetwork is put in operation, leading to a non-adaptive (fixed),low-complexity algorithm. Ideally the algorithm would usethe wavelength order that results in the least crosstalk. Eventhough the search for a satisfactory priority-list is performedoffline, it must still be done efficiently. The ordering problemaddressed in [11] was shown to be NP-hard because the searchspace grows as the factorial of the number of wavelengths perfiber link. This exhaustive search becomes intractable as thenumber of wavelengths becomes large. Let us consider thecomplexity of our optimal ordering problem before proposinga low-complexity alternative heuristic.

The optimization objective we consider is to find the wave-length order that minimizes the average crosstalk in the network



for given traffic statistics when using a FF wavelength assign-ment algorithm. Call arrivals are assumed to be Poisson andservice time is assumed to be exponential. Then the network isMarkovian and we can consider the steady-state distribution ofnetwork states. The states identify which channels are utilizedon each link in the network. The crosstalk at each network stateis determined by the wavelength ordering (mapping from wave-lengths to channels). Different orderings may have a differentcrosstalk impact for the same network state. Denoting the vectorof network states by (of size ) and the wavelength ordering

-vector by , the objective can be written as

(5)

where is the probability of network state andis the crosstalk power level if the network state is and wave-lengths are ordered as . Solving (5) directly involves an ex-haustive search of , which has cardinality !.

An alternative and equivalent way of writing this objective isto consider dependent only on the network state, andlet be determined by both the network state and the or-dering . The states now describe an actual wavelength usageand not a channel usage. The crosstalk of network wavelengthstate , , is equal to the value of for anotherchannel state for a FF algorithm using ordering . The ob-jective can now be written as an optimization over the stateprobabilities

(6)

The optimal ordering must assign the largest probability to thelowest crosstalk . A solution to this problem can beobtained by sorting the and assigning probabilities toeach. The complexity of sorting is . Thenumber of network states increases exponentially in the numberof wavelengths and the number of network links , .

In either case the complexity of finding an optimal solutionfor the wavelength ordering problem increases at least exponen-tially in the number of wavelengths in the network. A heuristiclow-complexity alternative is therefore proposed, as describedin Algorithm 1. The idea is that at any point the next wavelengthto be used should be as far as possible spectrally from any al-ready assigned wavelengths, since the crosstalk is modeled asexponentially decreasing with spectral separation. The first twowavelengths on the ordered list are the extreme points, i.e, wave-length slots labeled 1 and . Once ordered, these are removedfrom the unordered wavelength list. The algorithm creates theordered list by successively placing a wavelength from the re-maining unordered wavelength set that has the largest separa-tion from all the wavelengths in the ordered set. If there are sev-eral wavelengths with the same crosstalk impact, the wavelengthwhose sum of crosstalk contributions to all already-sorted wave-lengths is minimum is chosen, as stated on line 4 in Algorithm1. For example, if there are 8 wavelengths then the resulting or-dering is (1, 8, 4, 6, 2, 7, 3, 5). Again the term channel is usedhere to refer to the place in the ordered list, and is not the sameas the wavelength number; in our example channel 2 representswavelength 8.

Algorithm 1 Heuristic wavelength ordering algorithm.

1: Define two wavelength arrays, and , correspondingto ordered and successive wavelength sequences, wheresuccessive wavelengths are labeled .Initially set to and contains other wavelengths

2: Estimate the crosstalk power levelfor wavelength in to every in by

for some constant3: Create a set consisting of all candidate wavelengths

with the same minimum worst-case crosstalk level

4: Identify the next wavelength to add to the ordered list,. Append to

and remove it from5: Repeat from step 2 until is empty6: return ordered wavelength sequence

To estimate the complexity of this algorithm, we must find thecomplexity of each cycle through the loop; note that there is oneloop performed times. For the cycle, the size ofis and the size of is (variables defined in Algorithm1). The array is of size and the number of stepsneeded to compute is

. The size of is bounded by , so performing step 4in Algorithm 1 is of order . Thetotal complexity is then

(7)which is , only polynomial in .

The conventional FF wavelength assignment algorithm be-comes the FF with ordering (FFwO) algorithm by picking thenext candidate wavelength from the ordered array insteadof sequentially, as is typically done. For a QoS-guaranteed WAalgorithm, it is hoped that wavelength ordering increases thechance that the wavelength considered has a small number ofadjacent-port crosstalk terms. A wavelength ordering techniquecan also be integrated into the QoS-aware FF WA to decreasethe latency caused by BER estimation since it can increase theprobability that the first wavelength tried has sufficiently lowBER, thereby decreasing , the number of BER estimation cy-cles needed in (2).

We compare three QoS-aware and three QoS-guaranteedWAs, based on FF, RP, and FFwO. In the following section,we evaluate the performance of these WA algorithms for tworouting techniques, SP and ALT, in terms of blocking proba-bility for two large networks.

IV. SIMULATIONS AND RESULTS

We compare the algorithms presented above by means of sim-ulation since there are currently no known analytical techniquesfor evaluating complex RWA algorithms. The only known per-formance bound is that obtained when QoS factors are ignored,which is too loose a bound to be meaningful. The number ofcalls generated is more than in each simulation. Sim-ulation results are then valid to within a 95% confidence level.



Fig. 5. Network topologies used in simulation. (a) 16-node mesh toroid network (MESHnet) with identical link length of 100 kilometers and (b) topology of adownsized version of the NSF network (NSFnet), using link lengths 1/10 of their original size, with 14 nodes and 21 bidirectional links. The number on the linksrepresents the length of the links in kilometers. Each link is considered as a single span.

TABLE INETWORK SIMULATION PARAMETERS AND

EQUATIONS IN WHICH THEY ARE USED

The confidence intervals are not shown since they are all smallcompared to the scale of the plots.

The impact of physical impairments depends not only onthe physical layer parameters as described above, but also onthe network topology. A star network is more susceptible tocrosstalk because all LPs traverse one central node. A ring net-work is likely to suffer more from wavelength shortages as LPsmust share the same route. To assure that our results generalizeto various types of networks, we evaluate our WA techniques ontwo quite different large networks. The popular 16-node meshtoroid network is studied because of its high degree of symmetryand connectivity. We also consider the NSF network as a morepractical example to compare the WA algorithms.3 The two net-work topologies are depicted in Fig. 5. For each network, thebaseline parameter values used in the simulations are listed inTable I. We model the arrival process as Poisson, and the ser-vice time as exponentially distributed. The QoT requirement isa , as computed using the technique mentionedin Section II-C. The latency is estimated using the expressionsin Section II-B.

3The link lengths on the NSF network are scaled by 1/10 because the physicalimpairments would otherwise be too strong for our link design, which is notoptimized for long-haul communications.

The allowed timeout and the service time in wavelengthrouted optical networks depend heavily on the application. In aneffort to present numerically meaningful results, we have chosena timeout of 20 s with service time s, as might beneeded in e-science applications. The transmission delay, prop-agation delay and other delays are not included in the model.They can simply be added to the timeout threshold if necessary.The time required to perform one BER calculation, , dependsentirely on the hardware and software used. The baseline valueof was chosen so as to demonstrate the impact of latency andBER on the blocking probability. The results presented applyequally well to networks with different temporal requirementby simply scaling all time values ( , , , ) accord-ingly.4 For example, a scaling of 1/2000 results in a timeout of10 ms with a call service time of s, more appro-priate for short-term circuit-switched applications such as videodownloading; the simulation results apply directly to this caseas long as the arrival rate is increased to keep the load the same.

In the plots, the WA algorithms used are identified as QoS-aware (QoS-FF, QoS-RP, and QoS-FFwO) or QoS-guaranteed(FF, RP, and FFwO). The routing technique used is identified byappending either “SP” or “ALT” to the name of the WA algo-rithm, e.g., QoS-FF-SP or QoS-FF-ALT. The QoS-guaranteedWA algorithms try a single LP (if one exists) in SP or a primaryand an alternate LP (if the primary LP fails) in ALT using thecorresponding wavelength selection method; they do not repeat-edly try other candidate LPs unlike the QoS-aware techniques.They are thus not as powerful as the QoS-aware techniques, yetstill guarantee the QoS for every communicating LP, new andestablished.

For both QoS-guaranteed and QoS-aware WA algorithms, theblocking probability measured includes call requests droppeddue to wavelength blocking and to the failure of the chosenwavelength to satisfy the QoS (BER or latency) requirements.We define , , , and as the simulated overall

4Note that the physical model we have adopted for simulation is quite simpleand would not require extensive computational power. Employing a complexmodel considering all types of physical impairments as would be done in prac-tice would be prohibitive. The delay used in the computations is derived fromthe simulations, which is only indicative of what the real delay might be.



Fig. 6. � with BER and latency constraints for the six WA algorithmswhen SP routing is applied. (a) MESHnet with � � �� dB, � �

�� dB, and total traffic load of 160 Erlangs. (b) NSFnet with� � �� dB,� � �� dB, and total traffic load of 100 Erlangs.

blocking probability, the blocking probability caused by an un-satisfactory BER constraint, the blocking probability due to thetimeout constraint, and the wavelength blocking probability, re-spectively. In our simulation, each blocked event is counted aseither wavelength blocked , latency blocked , or BERblocked , so that .

A. Blocking Versus Processing Time

To see how the BER estimation delay affects network perfor-mance, we examine in Figs. 6 and 7 the various blocking prob-abilities for a range of for each routing and WA algorithm.Without loss of generality, we define a dimensionless parameter

as so that scales with ; this makes theresults directly applicable to any time scale. The vertical dashedline in the figures is used to indicate a common point in the dif-ferent simulations. In each figure the results are shown for themesh toroid network in subplot (a) and the scaled version of theNSF network in subplot (b), using the parameters as noted.

For SP routing shown in Fig. 6, QoS-FFwO-SP outperformsother QoS-aware WAs for and the QoS-FF-SPis better than QoS-RP-SP except in the long delay case. When

, the centralized controller is overloaded and theof the three QoS-aware WAs increases steeply because of theshort timeout. In this case, the for QoS-FF-SP and QoS-FFwO-SP grows faster than for QoS-RP-SP. QoS-FF-SP causesmore blocking than QoS-RP-SP when and QoS-FFwO-SP causes more blocking than QoS-RP-SP when

. QoS-FF-SP suffers most from long BER estimationtimes because it needs more time to find a LP fulfilling the QoTrequirement.

QoS-guaranteed SP WA algorithms have nearly constantperformance because they do not iteratively try other

candidates so that has less impact. FFwO-SP performs thebest, followed by RP-SP, and then FF-SP. FF-SP tries to packthe traffic in the same few low-index wavelengths and thereforemeets the most severe demultiplexer crosstalk impairments.The wavelength ordering technique successfully removes a

Fig. 7. � with BER and latency constraints for the six WA algorithmswhen ALT routing is applied. (a) MESHnet with � � �� dB, � �

��dB, and total traffic load of 160 Erlangs. (b) NSFnet with� � ��dB,� � �� dB, and total traffic load of 100 Erlangs.

fraction of the crosstalk, which helps FFwO-SP achieve a betterperformance than RP-SP or FF-SP.

In Fig. 7, the performance of the various WA algorithmsusing the fixed alternate routing algorithm are shown to besimilar to those for SP routing in Fig. 6. QoS-FFwO-ALT issuperior until . QoS-FF-ALT is worse thanQoS-RP if because dominates forlarge s, as shown in Table II.5 Compared to Fig. 6, each WAalgorithm using ALT routing outperforms the correspondingWA using SP routing because ALT can decrease [8]. Wealso observe that QoS-FFwO-SP is better than FFwO-ALT inthe 16-node mesh network, whereas FFwO-ALT is better thanQoS-FFwO-SP in the NSF network. Thus, the performancecomparison of SP and ALT for different WA algorithms isdependent of the network topology. However, the performanceimprovement from QoS-awareness is much larger than theimprovement from ALT routing algorithm. This is clearly seenby comparing the gap between FF-SP and FF-ALT with thegap between FF-SP and QoS-FFwO-SP.

We quantify the effect of the timeout and BER constraintsby listing selected results for and in Table II. Forall values of , QoS-FF-SP has the largest , followed byQoS-FFwO-SP and QoS-RP-SP. QosS-FFwO-SP benefits fromthe wavelength ordering technique and has nearly the sameas QoS-RP-SP. The basic idea of RP is randomly choosing chan-nels so the variable in (2) is lower for QoS-RP-SP than forQoS-FF-SP and QoS-FFwO-SP. This comes from the fact that,since RP blocks more requests than FF, there are (on average)less active paths in the network under RP and therefore theaverage is smaller. QoS-FFwO-SP enjoys a lower thanQoS-FF-SP because the number of trials in (2) for QoS-FFwO-SP is less than for QoS-FF-SP (and QoS-RP-SP). Thewavelength ordering technique improves the probability that thechannel first chosen (the first wavelength on the list availableend-to-end) is a feasible one (satisfies the QoT constraint) andthus QoS-FFwO-SP results in a smaller average latency than

5For this and subsequent tables, a “�” indicates insufficient simulation results.



TABLE II� AND � RESULTS FOR FIGS. 6 AND 7

QoS-FF-SP. Furthermore, since FF blocks more requests (onaverage) than FFwO, the network load under FF tends to belower. Therefore, the chance that a wavelength is free in FF ishigher than in FFwO, which generates more checking for BERand therefore is larger.

For the three QoS-guaranteed SP WA algorithms, FFwO-SPhas a lower than FF. Unlike the QoS-aware WAs, RP-SPperforms much better than FFwO-SP in terms of again be-cause of a lower ( in this case). However, ofFFwO-ALT is slightly better than of RP-ALT because av-erage of in RP-ALT is larger than that in FFwO-ALT ( canbe one or two in this case). In summary, the results show thatwavelength ordering improves the blocking probability by in-creasing the chances that the first channel selected meets theBER requirement and by decreasing the number of iterations inthe wavelength search.

Table II also shows that the BER blocking for FFwO (forboth SP and ALT) is significantly lower than for FF, whichjustifies our claim that wavelength ordering reduces crosstalk.Surprisingly, the QoS-aware WAs do not follow the same trendas the QoS-guaranteed WAs. For instance, QoS-FF-SP has thesame as QoS-FFwO-SP, but FF-SP is much worse thanFFwO-SP because FF only tests the first candidate and QoS-FFcan test other candidates so that QoS-FF finally does find a qual-ified LP (thereby incurring a larger , as seen in Table II).

QoS-RP-SP and -ALT have the highest among theQoS-aware WAs, yet RP is better than FF among the QoS-guar-anteed WAs. The reason, as noted previously [8], [9], [14], isthat the RP algorithm can reduce the physical impairments inexchange for more different wavelengths used, causing higherwavelength blocking. QoS-RP-SP and -ALT do poorly becausethey have fewer candidate LPs than QoS-FF and QoS-FFwO,which decreases their chances of finding a LP that satisfies theBER requirement. Notice that when is large, the of allWA algorithms decreases due to a decrease in effective networktraffic as a large percentage of calls have timed-out.

B. Blocking versus Network Traffic Load

To show how the twelve RWA algorithms perform in differentnetwork conditions, in Figs. 8 and 9 we plot the total blockingprobabilities while varying the total network load. Selected

Fig. 8. � with BER and latency constraints for the six WA algorithmswhen SP routing is applied, using � � �� . (a) MESHnet with � �

�� dB and � � �� dB. (b) NSFnet with � � �� dB and � �

�� dB.

and results of are shown in Table III. In all simulations,we have selected a BER estimation delay of ,corresponding to ms.

of SP routing is plotted in Fig. 8, showing that QoS-FFwO-SP performs best among the six SP WAs, yielding thelowest average blocking probability in all traffic load cases.QoS-FF-SP is second-best, except in light traffic ( 140 Erlangsfor the mesh network and 80 Erlangs for the NSF network).In the low traffic case, wavelength blocking is negligible and

is dominated by and . is nearly zero forQoS-RP-SP as shown in Table III, yet it is that is negli-gible for QoS-FF, as shown in Table III. Thus, for QoS-RP-SP

is bounded by and for QoS-FF-SP it is boundedby . for QoS-RP-SP is less than for QoS-FF-SP inthis simulation scenario, but this can easily reverse dependingon the delays and crosstalk levels. It can be seen that and

for QoS-FFwO-SP both diminish to nearly zero in thelow traffic situation. As the traffic increases, cannot be ne-glected and the performance of QoS-FF-SP surpasses that of



TABLE III� AND � RESULTS FOR FIGS. 8 AND 9

Fig. 9. � with BER and latency constraints for the six WA algorithmswhen ALT routing is applied, using � � �� . (a) MESHnet with� �

�� dB and � � �� dB. (b) NSFnet with � � �� dB and � �

�� dB.

QoS-RP-SP since QoS-FF-SP has lower than QoS-RP-SP[8]. Note that QoS-FF-SP and QoS-RP-SP are always worsethan QoS-FFwO-SP since the wavelength ordering techniquedecreases the adjacent-port crosstalk impairment without pro-ducing larger latency. Compared Fig. 8 with Fig. 9, the decreaseof between FF and QoS-FFwO is much larger than thatbetween FF and FF-ALT. QoS-aware WAs again are more pow-erful than ALT routing algorithm.

The results shown in Fig. 9 for ALT routing are similar tothose for SP in Fig. 8. Each ALT WA algorithm outperforms thecorresponding SP WA algorithm. The ALT algorithm can notonly efficiently decrease , but also decrease as shownin Table III. This is because ALT routing has another alternateroute that can be used during CAC so that the number of candi-date channels is larger than that for SP. Moreover, for ALTis not significantly increased because is decreased, from thefact that the traffic is spread to two routes decreasing the existingtraffic in each.

As expected, the performance of the three QoS-awareWAs for both SP and ALT is superior to the performance ofQoS-guaranteed WAs. Yet, the QoS-guaranteed WAs have afaster response compared to their corresponding QoS-awareWAs, as seen in Table III, where FF, FFwO, and RP havenegligible , in either routing scheme. QoS-FF-ALT causesthe highest among all WAs. For QoS-RP, for both SP andALT routing, the randomness in wavelength selection tends tospread the calls throughout the spectrum so that the probabilitythat the chosen wavelength meets crosstalk from other in-bandsignals is less than that for QoS-FF. Thus, the average valueof in (2) for QoS-RP becomes less than that for QoS-FF,since for QoS-RP is less than that for QoS-FF. However, thewavelength ordering technique diminishes the probability thatthe chosen wavelength meets crosstalk from adjacent wave-lengths and decreases the average of . Therefore, QoS-FFwOsuffers a similar delay as QoS-RP so that its is similar tothat of QoS-RP, and yet remains smaller than that of QoS-FF,as shown in Table III.

In Table III, QoS-FFwO-ALT has the lowest amongall of algorithms. Among the QoS-guaranteed algorithms,FFwO-ALT is the best algorithm. Generally, the performanceof FFwO WA is better than that of RP, which is better thanthat of FF, as expected. FFwO is better than FF because theordering technique alleviates crosstalk from the demultiplexers.Thus, the for QoS-FF falls lower than for QoS-RP inTable III. The gap between QoS-aware FF and QoS-aware RP isdiminished as the traffic increases, where crosstalk introducedby high traffic load may lead to all unsatisfactory candidateLPs in FF. Note that QoS-aware FF may have lower thanQoS-aware FFwO in light traffic situation, because QoS-awareFF experiences more total blocking. In other words, QoS-awareFF blocks more calls thereby decreasing the effective networkload in the network so that it experiences less physical impair-ments than QoS-aware FFwO.

C. Blocking versus Crosstalk Level

As crosstalk is considered the dominant physical impairmentin this paper, it is important to determine how each algorithmperforms under different crosstalk power levels. The totalblocking probabilities for the twelve RWA algorithms are



TABLE IV� AND � RESULTS FOR FIGS. 10 AND 11

Fig. 10. � with BER and latency constraints for the six WA algorithmswhen SP routing is applied, using � � �� . (a) MESHnet with � �

�� dB and traffic load of 160 Erlangs. (b) NSFnet with � � �� dB andtraffic load of 100 Erlangs.

plotted in Figs. 10 and 11 for a fixed adjacent-port crosstalk levelas the switching fabric crosstalk level varies from

50 dB to 30 dB. Selected values of and are shownin Table IV.

As seen in Fig. 10, among the SP routed systems,QoS-FFwO-SP has the best performance and QoS-FF-SP isthe second-best algorithm. QoS-RP-SP always performs worstbecause it has the highest , even though QoS-RP-SP hasmuch better than QoS-FF-SP, as shown in Table IV. As thelevel of crosstalk increases, QoS-aware SP WA algorithms needmore time to obtain a workable LP. for QoS-FFwO-SPincreases faster than for QoS-RP-SP and QoS-FF-SP as shownin Table IV. At the low level of crosstalk, wavelength orderinghelps QoS-FFwO-SP setup the LPs rapidly. In strong crosstalk( larger than 35 dB ),6 7 physical impairments fromforce QoS-FFwO-SP to iterate more times to find a workable

6Crystalatch 16�10 Fiberoptic Matrix Switch. [Online]. Available: http://www.agiltron.com/PDFs/CL%2016x10%20switch_2.pdf

7Nano Speed 2�2 Solid-State Fiber-Optic Switch. [Online]. Available: http://www.agiltron.com/PDFs/NS2x2specsheet.pdf

Fig. 11. � with BER and latency constraints for the six WA algorithmswhen ALT routing is applied, using � � �� . (a) MESHnet with� �

�� dB and traffic load of 160 Erlangs. (b) NSFnet with � � �� dB andtraffic load of 100 Erlangs.

LP so that QoS-FFwO-SP has the nearly the same delay asQoS-FF-SP.

In Fig. 11, the six ALT WA algorithms show the same trendsas the six SP WA algorithms in Fig. 10. As we expected, when

is large, for QoS-FFwO-ALT is sometimes largerthan for QoS-RP-ALT because for QoS-FFwO-ALT islarger than for QoS-RP-ALT, as shown in Table IV.

RP still benefits from its randomness property in all situa-tions. We can observe this phenomenon from the three QoS-guaranteed WA algorithms for both routing algorithms in Fig. 10and 11. Note that and for QoS-guaranteed WA algo-rithms are negligible and is the dominant term of .The and for FF is worst. FFwO is better than RPwhen dB in SP routing and dB inALT. When is large, FFwO is worse than RP since FFwOcan only alleviate the crosstalk effects from the demultiplexerand yet results in a large , as shown in Table IV. Note thatcurrent switching devices based on MEMS technology can pro-vide switching crosstalk levels as low as dB [30].Thus, FFwO outperforms RP in current devices.



Fig. 12. Total blocking probability with BER and latency constraints for dif-ferent routing path length for the six WA algorithms when SP routing is applied,using � � �� . (a) MESHnet with� � ��dB,� � �� dB, andtraffic load of 160 Erlangs. (b) NSFnet with� � �� dB,� � �� dB,and traffic load of 100 Erlangs.

D. Blocking versus Path Length

The blocking probabilities for different path lengths (as mea-sured by the number of hops) are shown in Fig. 12 to verifythat fairness amongst node pairs is not compromised by usinga particular WA algorithm. Only SP routing is considered herebecause the primary and alternate routes may have differentlengths. Clearly, longer paths are more likely to be blocked usingany WA algorithm. Yet QoS-FFwO-SP performs best among allWA algorithms for all path length values tested. Second bestis QoS-FF-SP, always beating QoS-RP-SP. Among the QoS-guaranteed SP algorithms, for all path length cases, FFwO-SPoutperforms RP-SP, and FF-SP performs the worst. All resultsdemonstrate that wavelength ordering effectively decreases theblocking probabilities for paths with different lengths.

V. CONCLUSION

In this paper, we study the impact of guaranteeing QoS, in-corporating both BER and latency constraints, on the perfor-mance of WA algorithms in SP and ALT routing. We proposea new heuristic offline wavelength ordering algorithm to wiselyallocate the wavelengths to calls/requests in order to minimizecrosstalk due to adjacent wavelength power leaking through theWDM demultiplexers. We show that this technique not only al-leviates the effects of physical impairments, but also decreasesthe latency in QoS-aware algorithms, over a wide range of net-work parameters. Wavelength ordering can be done without anyextra hardware or run-time computational expense. Because ofthis ordering technique, QoS-aware FFwO performs better thanother QoS-aware WAs in all practical cases when SP or ALTrouting is applied. QoS-aware FF outperforms QoS-aware RPin many circumstances even though QoS-aware RP induces lessdelay than QoS-aware FF.

We have studied the performance of a variety of WA algo-rithms under several different network conditions and applica-tion timeout constraints. We’ve shown that our QoS-aware al-gorithms perform uniformly better under all conditions. Notethat when the timeout constraints aren’t severe, the difference

in performance between our algorithms and traditional ones iseven larger.

If the design of the centralized network controller allows forthe extra complexity of a QoS-aware RWA algorithm, QoS-aware FFwO should be selected over the other QoS-aware WAsconsidered in SP or ALT routing, for all traffic and levels ofphysical impairments cases. If the use of a simpler QoS-guaran-teed WA algorithm is necessary, we suggest the FFwO algorithmfor networks using switches with dB. Surprisingly,the power of QoS-aware WA is strong and it brings a larger per-formance improvement than ALT routing does.

The QoS-aware and QoS-guaranteed WA algorithms de-scribed here can be applied to other routing algorithms toimprove the search for a better LP with higher probabilityof fulfilling the QoS requirements. Adding such QoS-awarerouting to the WA algorithms presented here is a subject ofongoing work. Indeed, for the RWA problem, the number ofpossible choices to test is much larger than for the WA problem.In those cases, the computation power may become an evenmore important constraint.

Centralized control can become prohibitive for larger (con-tinental) networks. Distributed schemes that incorporate QoSconstraints are also presently being investigated.

REFERENCES

[1] P. Green, “Optical networking update,” IEEE J. Sel. Areas Commun.,vol. 14, no. 5, pp. 764–779, Jun. 1996.

[2] B. Mukherjee, “WDM optical communication networks: Progressand challenges,” IEEE J. Sel. Areas Commun., vol. 18, no. 10, pp.1810–1824, Oct. 2000.

[3] J. Strand and A. Chiu, “Impairments and other constraints on opticallayer routing,” Internet Informational RFC 4054, May 2005, 2005 [On-line]. Available: http://tools.ietf.org/html/rfc4054

[4] R. Martinez, C. Pinart, F. Cugini, N. Andriolli, L. Valcarenghi, P.Castoldi, L. Wosinska, J. Comellas, and G. Junyent, “Challenges andrequirements for introducing impairment-awareness into the manage-ment and control planes of ASON/GMPLS WDM networks,” IEEECommun. Mag., vol. 44, no. 12, pp. 76–85, Dec. 2006.

[5] E. L. Goldstein and L. Eskildsen, “Scaling limitations in transparentoptical networks due to low-level crosstalk,” IEEE Photon. Technol.Lett., vol. 7, pp. 93–94, Jan. 1995.

[6] B. Ramamurthy, D. Datta, H. Feng, J. Heritage, and B. Mukherjee,“Impact of transmission impairments on the teletraffic performance ofwavelength-routed optical networks,” J. Lightw. Technol., vol. 17, no.10, pp. 1713–1723, Oct. 1999.

[7] S. Subramaniam, M. Azizoglu, and A. Somani, “All-optical networkswith sparse wavelength conversion,” IEEE/ACM Trans. Netw., vol. 4,no. 4, pp. 544–557, Aug. 1996.

[8] H. Zang, J. Jue, and B. Mukherjee, “A review of routing and wave-length assignment approaches for wavelength-routed optical WDMnetworks,” Opt. Netw. Mag., no. 1, pp. 47–60, 2000.

[9] T. Deng, S. Subramaniam, and J. Xu, “Crosstalk-aware wavelengthassignment in dynamic wavelength-routed optical networks,” in Proc.IEEE Int. Conf. Broadband Netw., San Jose, CA, Oct. 2004, pp.140–149.

[10] Y. Pointurier, M. Brandt-Pearce, T. Deng, and S. Subramaniam, “FairQoS-aware adaptive routing and wavelength assignment in all-opticalnetworks,” presented at the IEEE Int. Conf. Commun. (ICC), Istanbul,Turkey, Jun. 2006.

[11] I. Fonseca, J. R. Almeida, H. Waldman, and M. Ribeiro, “Meetingoptical QoS requirements with reduced complexity in dynamic wave-length assignment,” in Proc. IEEE Int. Conf. Broadband Netw., Oct.2004, pp. 184–193.

[12] J. He and M. Brandt-Pearce, “Dynamic wavelength assignment usingwavelength spectrum separation for crosstalk limited networks,” inProc. IEEE Int. Conf. Broadband Netw., San Jose, CA, USA, Oct.2006.

[13] R. Cardillo, V. Curri, and M. Mellia, “Considering transmission im-pairments in configuring wavelength routed optical networks,” in Proc.IEEE/OSA Opt. Fiber Commun. Conf., Mar. 5–10, 2006, p. 3.



[14] J. He, M. Brandt-Pearce, Y. Pointurier, and S. Subramaniam, “Adaptivewavelength assignment using wavelength spectrum separation for dis-tributed optical networks,” presented at the IEEE Int. Conf. Commun.(ICC), Glasgow, U.K., Jun. 2007.

[15] J. He, M. Brandt-Pearce, Y. Pointurier, and S. Subramaniam, “QoT-aware routing in impairments-constraint optical networks,” presentedat the GLOBECOM, Washington, DC, Nov. 2007.

[16] C. Politi, C. Matrakidis, A. Stavdas, V. Anagnostopoulos, and M.Gunkel, “Cross layer routing in transparent optical networks,” in Proc.Conf. Opt. Fiber Commun. Nat. Fiber Optic Eng. Conf., Mar. 25–29,2007, pp. 1–3.

[17] C. Pinart and H. Harai, “On using optical-layer link information pa-rameters in distributed impairment constraint-based routing,” in Proc.Conf. Opt. Fiber Commun. Nat. Fiber Optic Eng. Conf., Mar. 25–29,2007, pp. 1–3.

[18] I. Tomkos, S. Sygletos, A. Tzanakaki, and G. Markidis, “Impairmentconstraint based routing in mesh optical networks,” in Proc. Conf. Opt.Fiber Commun. Nat. Fiber Optic Eng. Conf., Mar. 25–29, 2007, pp.1–3.

[19] Y. Zhai, Y. Pointurier, S. Subramaniam, and M. Brandt-Pearce, “QoS-aware RWA algorithms for path-protected DWDM networks,” in Proc.Conf. Opt. Fiber Commun. Nat. Fiber Optic Eng. Conf., Mar. 25–29,2007, pp. 1–3.

[20] R. Ramaswami, “Optical networking technologies: What worked andwhat didn’t,” IEEE Commun. Mag., vol. 44, no. 9, pp. 132–139, Sep.2006.

[21] L. Ruan and D. Du, Optical Networks—Recent Advances. New York:Kluwer, 2001.

[22] J. He, M. Brandt-Pearce, and S. Subramaniam, “Connection provi-sioning in QoT-guaranteed distributed all-optical networks,” presentedat the IEEE 4th Int. Conf. Broadband Commun., Netw., Syst., Raleigh,NC, Sep. 2007.

[23] R. Ramaswami and K. N. Sivarajan, Optical Networks: A PracticalPerspective, 2nd ed. New York: Morgan Kaufmann, 2002.

[24] L. Shen and B. Ramamurthy, “Centralized versus distributed con-nection management schemes under different traffic patterns inwavelength-convertible optical networks,” in Proc. IEEE Int. Conf.Commun. , Apr. 2002, pp. 2712–2716.

[25] J. He, M. Brandt-Pearce, and S. Subramaniam, “BER and latency con-strained QoS-aware distributed routing for all-optical networks,” pre-sented at the IEEE Int. Conf. Commun. Netw. China, Shanghai, China,Aug. 2007.

[26] H. Zang, J. Jue, L. Sahasrabuddhe, R. Ramamurthy, and B. Mukherjee,“Dynamic lightpath establishment in wavelength routed WDM net-works,” IEEE Commun. Mag., vol. 39, no. 9, pp. 100–108, Sep. 2001.

[27] H. Takahashi, K. Oda, and H. Toba, “Impact of crosstalk in an arrayed-waveguide multiplexer on � � � optical interconnection,” J. Lightw.Technol., vol. 14, no. 6, pp. 1097–1105, Jun. 1996.

[28] G. Agrawal, Fiber-Optic Communication Systems, 3rd ed. New York,USA: Wiley, 2002.

[29] J. He and M. Brandt-Pearce, “RWA using wavelength ordering forcrosstalk limited networks,” presented at the IEEE/OSA Opt. FiberConf., Anaheim, CA, Mar. 2006.

[30] X. Ma and G.-S. Kuo, “Optical switching technology comparison: Op-tical MEMS versus other technologies,” IEEE Commun. Mag., vol. 41,no. 11, pp. S16–S23, Nov. 2003.

Jun He (S’04-M’09) received the B.S.E.E. andM.S.E.E degrees in electrical engineering fromHuazhong University of Science and Technology,Wuhan, China, in 2002 and 2004, respectively, andthe Ph.D. degree in Electrical Engineering fromthe Charles L. Brown Department of Electrical andComputer Engineering at the University of Virginia,Charlottesville, United States, in 2008.

He is currently a Postdoctoral Research Associatein the Department of Computer Science at TexasState University, San Marcos. His research interests

include optical communication, network modeling, cross-layer issues in com-munication networks, wireless communication, and cyber-physical systems.

Maïté Brandt-Pearce received the B.S.E.E., M.E.E,and Ph.D. degrees in electrical engineering from RiceUniversity, Houston, TX, in 1985, 1989, and 1993,respectively.

She worked with Lockheed in support of NASAJohnson Space Center from 1985 to 1989. In 1993,she joined the Charles L. Brown Department ofElectrical and Computer Engineering at the Uni-versity of Virginia, Charlotesville, where she iscurrently a Full Professor. In 2005, she spent hersabbatical at the Eurécom Institute in Sophia An-

tipolis, France. Her research interests lie in the mathematical description anddesign optimization of communication systems with multiple simultaneouslycomponents from different sources. This interest has found applications ina variety of research projects including spread-spectrum multiple-accessschemes, multiuser demodulation and detection, study of nonlinear effects onfiberoptic multiuser/multichannel communications, optical networks subject tophysical layer degradations, free-space optical multiuser communications, andradar signal processing and tracking of multiple targets.

Dr. Brandt-Pearce is the recipient of an NSF CAREER Award, an NSF RIA,and an ORAU Junior Faculty Enhancement Award. She is a co-recipient of BestPaper Awards at the ICC 2006 Symposium on Optical Systems and Networks.She is a member of Tau Beta Pi, Eta Kappa Nu, and a senior member of the IEEE.She was an Associate Editor for the IEEE TRANSACTIONS ON COMMUNICATIONS

from 1999 to 2006. She has served on the technical program committee for nu-merous conferences and was the 2007 Technical Chair for the Asilomar Con-ference on Signals, Systems, and Computers.

Suresh Subramaniam (S’95–M’97–SM’07) re-ceived the Ph.D. degree in electrical engineeringfrom the University of Washington, Seattle, in 1997.

He is a Full Professor in the Department of Elec-trical and Computer Engineering at the George Wash-ington University, Washington, DC. His research in-terests are in the architectural, algorithmic, and per-formance aspects of communication networks, withparticular emphasis on optical and wireless networks.He is a co-editor of the books Optical WDM Net-works—Principles and Practice (Kluwer, 2000) and

Emerging Optical Network Technologies: Architectures, Protocols, and Perfor-mance (Springer, 2005).

Dr. Subramaniam has been on the program committees of several conferencesincluding Infocom, ICC, Globecom, and OFC, and served as TPC Co-Chair forthe optical networks symposia at Globecom 2006 and ICC 2007. He serves onthe editorial boards of IEEE/ACM TRANSACTIONS ON NETWORKING, OpticalSwitching and Networking, and KICS Journal of Communications and Net-works. He is a co-recipient of Best Paper Awards at the ICC 2006 Symposiumon Optical Systems and Networks, and at the 1997 SPIE Conference on All-Op-tical Communication Systems.


462 journal of lightwave technology, vol. 27, no. 5, march 1

Documents