IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, …alfuqaha/publications/J6.pdfAL-FUQAHA et al.: ROUTING FRAMEWORK FOR ALL-OPTICAL DWDM METRO AND LONG-HAUL TRANSPORT NETWORKS 1445

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 22, NO. 8, OCTOBER 2004 1443

Routing Framework for All-Optical DWDM Metroand Long-Haul Transport Networks With Sparse

Wavelength Conversion CapabilitiesAla I. Al-Fuqaha, Member, IEEE, Ghulam M. Chaudhry, Senior Member, IEEE,

Mohsen Guizani, Senior Member, IEEE, and Miguel A. Labrador, Senior Member, IEEE

Abstract—In this paper, we propose a novel routing frameworkfor all-optical dense wavelength-division-multiplexing transportnetworks with sparse wavelength conversion capabilities. Therouting framework includes an integer linear programming for-mulation to handle the static lightpath establishment problem anda novel open shortest path first protocol extension that advertisesthe availability of wavelength usage and wavelength conversionresources. Our routing framework addresses the limitations ofthe extensions presented in the literature because it also includes:1) an efficient flooding protocol that is suitable for the dynamicnature of these networks and 2) an efficient route and wavelengthcomputation engine that minimizes connection costs withouthindering the blocking probability.

Index Terms—Fuzzy, integer linear programming (ILP),link-state, link-state advertisements (LSAs), open shortest pathfirst (OSPF), routing and wavelength assignment (RWA), sparsewavelength conversion, update policies.

I. INTRODUCTION

THE DEMAND for more bandwidth is steadily increasingdespite the hard times facing telecommunication equip-

ment manufacturers and carriers. This demand motivated theindustrial and research communities alike to believe that re-moving the electronic components from optical transport net-works and using the dense wavelength-division-multiplexing(DWDM) technology are key to upgrade the capacity of thesenetworks. This led these communities to embark on the task ofdeveloping high capacity, modular, scalable, and flexible all-op-tical DWDM transport networks with rich monitoring and man-agement capabilities. However, the realization of these networksrequires the introduction of many new protocols and mecha-nisms to control and manage their resources.

Many new protocols and mechanisms have been introducedtoward this end. However, these protocols were introduced at avery fast pace and they still need to be evaluated, refined and

Manuscript received September 1, 2003; revised December 1, 2003.A. I. Al-Fuqaha is with LAMBDA Optical Systems, Inc., Reston, VA 20190

USA (e-mail: [email protected]).G. M. Chaudhry is with the School of Computing and Engineering, University

of Missouri, Kansas City, MO 64110 USA (e-mail: [email protected]).M. Guizani is with the Computer Science Department, Western Michigan

University, Kalamazoo, MI 49008 USA (e-mail: [email protected]).M. A. Labrador is with the Computer Science and Engineering Department,

University of South Florida, Tampa, FL 33620 USA (e-mail: [email protected]).

Digital Object Identifier 10.1109/JSAC.2004.830381

sometimes reinvented. The premise of generalized multipro-tocol label switching (GMPLS) is to provide a common controlplane (signaling and routing) for networks comprised of devicesthat switch in different domains: packet, time, wavelength, andfiber. At first glance, this approach might seem to reduce muchof the network complexities by eliminating many of the con-trol plane protocols that are currently in use and replacing themwith a common control plane (i.e., GMPLS). On the contrary,we believe that this approach will result in having a single butvery complex control plane (i.e., based on GMPLS) that tries tobe generic enough to deal with the different switching technolo-gies but fails to deal with some of the issues that are particularlyimportant to some of these technologies.

While all-optical DWDM transport networks offer newcapabilities, several challenges are introduced beyond thoseknown in traditional electro-optical networks. In this paper,we introduce a framework to handle connection routing andwavelength assignment operations in such networks. In thenext section, we provide the motivation for a new transportnetwork architecture. Section III provides an introduction to therouting and wavelength assignment (RWA) problem in all-op-tical DWDM networks with and without the lambda continuityconstraint. Section IV provides an integer linear programming(ILP) formulation for the RWA problem in DWDM networkswith sparse wavelength conversion capabilities. Section Vpresents an extension to the open shortest path first (OSPF)routing protocol that enables it to handle routing in all-opticalDWDM networks regardless of their wavelength conversioncapabilities. Section VI presents two new link-state originationpolicies to advertise the availability of the wavelength andconversion resources throughout the optical network domain.Section VII introduces a route computation engine based onfuzzy logic that efficiently selects paths for connection requestsin optical networks given the wavelength conversion constraintsthat might exist at each node. Simulation results are presentedin this section to compare our proposed fuzzy heuristic withother approaches used in the literature. Finally, Section VIIIdiscusses our findings and future extensions of this work.

II. NEED FOR A NEW TRANSPORT NETWORK ARCHITECTURE

A quick review of the overlay-based architecture of today’stransport networks reveals the inefficiencies associated withthem. Fig. 1(a) depicts this architecture, its overlays, and thedata rates used at the interfaces between these overlays. Each

0733-8716/04$20.00 © 2004 IEEE

1444 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 22, NO. 8, OCTOBER 2004

Fig. 1. (a) Conventional transport networks: Each signal is terminated at each B-DCS and ADM node. (b) Future transport systems with all-optical OTS systems,the released capacity on the B-DCS and ADM nodes can be used to extend the life of the network.

overlay is faster than the overlay on top. This architecture hasmany disadvantages. A lot of inefficiencies are introduced bythe multiple overlays, for example the protocol overhead tocarry Internet protocol (IP) traffic over asynchronous transfermode (ATM) over synchronous optical network (SONET)over DWDM totals to 22% of the bandwidth. Overlays do notoften work in concert; for example, every overlay runs at itsown speed resulting in low-speed devices not being able to fillup the wavelength bandwidth. Also, when detecting failures,overlays compete to perform protection. The optical transportsystems (OTSs) in the optical overlay do not provide automatedprovisioning resulting in an architecture that does not scalewell with increasing demand. SONET ADMs are advanta-geous for constant bit rate traffic but introduce undesirablelatencies for bursty traffic. Another drawback of the traditionaloverlay-based architecture is that SONET ADMS are inflexibleand costly since handling a higher level optical signal (e.g.,

OC-768) means replacing the current SONET-based equipmentwith a new one. Also, in this architecture, each switch thatdemultiplexes the signals will need an electrical network ele-ment for each channel even if the traffic on that channel is notdropping at the site. For all these reasons, another architecturethat minimizes the number of overlays in the transport networkis needed.

In order to overcome these inefficiencies, the industrialand research communities believe that the introduction of anoverlay of OTSs that support automated circuit provisioningand is transparent to transmission signal formats and data ratesis key in order to upgrade the capacity of today’s transportnetworks. Fig. 1(b) illustrates this new architecture. This newarchitecture allows IP-based services to be carried directly overthis new overlay reducing all the inefficiencies associated withtransporting these services over frame-relay over ATM overSONET over DWDM. Since Internet traffic projections expect

AL-FUQAHA et al.: ROUTING FRAMEWORK FOR ALL-OPTICAL DWDM METRO AND LONG-HAUL TRANSPORT NETWORKS 1445

that IP-based services will constitute a large portion of futureInternet traffic, this architecture offers huge advantages andseveral cost-effective service offerings and features such asautomated circuit provisioning, transparency to signal formatsand data-rates, unprecedented capacity offerings, protection,and efficient transport of IP-based services.

III. RWA IN NETWORKS WITH SPARSE WAVELENGTH

CONVERSION CAPABILITIES

One of the most important goals in this new transport net-work architecture is the support of real-time provisioning. Cur-rently, service providers have to go through a lengthy and te-dious manual process in order to satisfy a client’s request to es-tablish a lightpath. The realization of this vision, however, de-pends on many factors. Given a request for an optical channel,the RWA problem must be solved so that a route and a wave-length or lambda is assigned to each request from source to des-tination.

The RWA is an NP-complete problem that is usually dividedto the more manageable routing and wavelength assignmentsubproblems. For the routing subproblem, three main ap-proaches are known: fixed routing, adaptive routing, andsemi-adaptive routing [3], [5], [10], [11], [14]. For the wave-length assignment subproblem, several heuristics have beenproposed such as first-fit, MAX-SUM, least loaded, mostused, relative capacity loss, distributed relative capacity loss,and many others [14]. In addition, total cost-based selection,balanced cost-based selection, and future cost-based selectionheuristics that solve both problems in an integrated mannerhave been recently introduced in [12].

In the absence of wavelength converters, a lightpath must beestablished from a source to a destination using the same wave-length (lambda); this is a wavelength constraint network. On theother hand, with wavelength converters, lightpaths can be con-verted to different wavelengths, leading to an expected lowercall blocking probability.

Lightpath requests are commonly classified as static or dy-namic. With static requests, users’ demands are known in ad-vance and the provisioning problem is to set up lightpaths tosatisfy all the requests, while minimizing the amount of networkresources, such as the number of wavelengths and wavelengthconverters used by the lightpath. In this case, the RWA problemis known as the static lightpath establishment (SLE) problem.With dynamic requests, connections are requested in a dynamicfashion and the RWA problem will try to establish lightpathsthat minimize the blocking probability. This is known as the dy-namic lightpath establishment (DLE) problem.

Optical switches can be of two different types accordingto their conversion capabilities. Full wavelength conversionswitches are those that can convert an incoming wavelengthto any outgoing wavelength and in addition, the number ofconverters is equal to the total number of outgoing wavelengths.On the other hand, partial wavelength conversion switches areequipped with an optimal number of wavelength convertersto minimize the high cost of these devices. Several studieshave already considered the case of networks with wavelengthconverters. For example, [8], [12], and [13], consider the case

of optimal converter placement, where the switches have full orlimited conversion capabilities. Lately, it has been shown thatthe RWA and switch placement problems, which are usuallysolved separately, should be considered in an integrated manner[9]. In this paper, we assume sparse and limited wavelengthconversion resources. By sparse converter resources, we meanthat the optical network might not have enough wavelengthresources to satisfy every request requiring wavelength con-versions. By limited wavelength converter resources, we meanthat the wavelength converter resources installed in the opticalnetwork might not have the capability to convert any inputwavelength to any output wavelength. It has been shown thatnetworks with sparse and limited conversion capabilities canachieve similar blocking probability in a more cost-effectivemanner [13] than networks with full wavelength conversion.

IV. ILP FORMULATION FOR THE RWA PROBLEM IN

NETWORKS WITH SPARSE WAVELENGTH

CONVERSION CAPABILITIES (RWA-SWC)

In this section, we present an ILP formulation to solve theRWA problem in networks with sparse wavelength conversioncapabilities. In this formulation, the goal of the objective func-tion is to minimize the total cost of all lightpaths that need to beestablished in the optical network. As stated before, this is anNP-complete problem. In order to formulate the problem, let usdefine the following.

• : Number of switches.• : Number of links.• : Number of wavelengths per link.• : Total number of lightpaths that need to be established.• : Number of source-destination pairs.• : Vector of size , where ele-

ment represents the number of requested lightpaths be-tween the th source-destination.

• : Vector of size , where el-ement represents the number of all possible paths be-tween the th source-destination pair.

• : Vector of size , where el-ement represents the number of wavelength convertersinstalled on the th node.

• : A list of vectors that rep-resent the paths on which each of the source-destinationpairs can be routed, is the th vector of the list.Element represents the th path on which the thsource-destination pair can be routed. These paths can beenumerated using the -shortest paths algorithm. Noticethat two paths are considered to be distinct if they gothrough different fibers or different wavelengths in theirroute from source to destination.

• : A list ofmatrices that represent the usage of the link resources

by the different paths, matrix is the th matrix of thelist. Element if the th path between the thsource-destination pair uses link , otherwise, .

• : A listof matrices that represent the usage of the wavelengthconversion resources by the different paths, matrix is


the th matrix of the list. Element if the th pathbetween the th source-destination pair uses a wavelengthconverter that is installed on node , otherwise, .

• : A list of vectors that rep-resent the cost of the different paths, vector is the thvector of the list. Element is the cost of the th path be-tween the th source-destination pair.

• : A listof matrices that represent the usage of the wavelengthresources (lambdas) by the different paths, matrix isthe th matrix of the list. Element if the th pathbetween the th source-destination pair uses wavelength

on link , otherwise, .• : A list of vectors that rep-

resent the cost of the different paths, vector is the thvector of the list. Element if the th path be-tween the th source-destination pair is selected, other-wise, .

The objective function of the RWA problem in networks withsparse wavelength conversion capabilities can be defined tominimize the total cost of establishing all requested lightpaths.The RWA-SWC problem is then formulated as follows:

Minimize

Subject to the following constraints:

(1)

(2)

(3)

(4)

(5)

In this formulation, the symbol indicates the transpose op-eration. Equation (1) indicates that a path can be selected or notselected (binary variable). Equation (2) indicates that all the re-quested lightpaths need to be established for a solution to befeasible. Equation (3) verifies that no more than wavelengthsare used on a single link. Equation (4) verifies that the wave-length conversion capability constraints are respected. Finally,(5) guarantees that no more than one connection is carried onany given wavelength of all links in the network.

In order to use the formulation presented above on reason-able size networks, we propose a pruning strategy that aims atreducing the search space of possible routes and wavelengthsunder the scenario of static requests or static lightpath estab-lishment (SLE). This pruning strategy aims at:

1) limiting the possible routes between a givensource-destination pair;

2) limiting the possible wavelengths to only those that canbe generated by the tunable lasers’ and wavelength con-verters’ technologies installed in the network;

3) limiting the possible wavelengths to be the same be-fore and after nodes that do not support wavelengthconversion;

4) limiting the possible wavelengths to a subset of the wave-lengths that the DWDM links can support.

The reduced search space is then presented to the ILP for-mulation, which selects the routes and wavelengths that needto be assigned to the given connections. The result is that theseconnections are routed throughout the optical network with theleast possible cost obeying the wavelength conversion restric-tions present in the network domain.

The difficulty of any ILP problem depends on the number ofvariables and constraints in that problem. The factors that deter-mine the number of variables and constraints used in the aboveformulation are the number of connections that need to be estab-lished, the number of nodes in the network, the number of linksin the network, the number of nodes that possess wavelengthconversion resources, the type of wavelength conversion usedwithin the network, the number of paths that need to be consid-ered between a given source-destination pair, and the number ofwavelengths that can be carried on a single link. The followingtwo equations provide a simple estimate of the number of vari-ables and constraints involved in the ILP problem:

Number of Variables

Number of Constraints

wherenumber of lightpath requests that need to be estab-lished on the network;average number of possible routes that can be consid-ered between a given source-destination pair;number of wavelengths that can be carried over thelinks of the network;average number of hops used to route a given connec-tion;percentage of wavelength options that can be preelim-inated due to technology constrains or due to the lackof wavelength conversion resources;percentage of wavelength options that can be preelim-inated due to the user’s educated decision that a subsetof the supported wavelengths can be used to route allthe connections in hand.

The above equations also hold for networks with the wave-length continuity constraint by substituting . The processof estimating can sometimes be complicated, in such casesupper and lower bounds can be utilized to get a bounded esti-mate of the number of variables and constraints involved in theILP problem.

Typical ILP problems found in real-life situations have 2000variables and 4000 constrains. As a quick rule of thumb, theabove formulation will be helpful as long as the number of vari-ables and the number of constraints are around these typicalnumbers. Figs. 2 and 3 illustrate the relationship between thenumber of variables and constraints involved in the ILP problemand and , respectively. Notice the effect that andhave on reducing the number of variables and constraints in-volved in the ILP problem.

Table I illustrates two simple scenarios to which we appliedthe ILP formulation presented above. Table I also indicates theoptimal resources that need to be allocated to each lightpath.


Fig. 2. Number of variables and constraints versus P1. For lightpaths = 20,K = 2, W = 16, H = 1:6, and P2 = 0:2.

Fig. 3. Number of variables and constraints versus P2. For lightpaths = 20,K = 2, W = 16, H = 1:6, and P1 = 0:3.

TABLE IEXAMPLES OF ROUTE, WAVELENGTH, AND CONVERTER ASSIGNMENT

IN SWC NETWORKS

Fig. 4 shows the topology of the network on which the lightpathsindicated in Table I need to be established. In this example, eachwavelength converter is assumed to have a cost of 100. We im-plemented the -shortest paths algorithm to enumerate the dif-ferent paths for the ILP formulation and then we used CPLEXto solve the formulation.

It should be noted here that as the number of variables andconstraints involved in the ILP problem grows, the formulation

Fig. 4. Sample network with SWC capabilities.

presented in this section becomes of limited use. In Section VII,we present a RWA strategy to handle such networks.

V. OSPF ROUTING EXTENSION IN SUPPORT OF

ALL-OPTICAL DWDM NETWORKS WITH SPARSE

WAVELENGTH CONVERSION CAPABILITIES

As explained previously, a requested optical lightpathneeds to be assigned a route and a single or a set of wave-lengths throughout the optical network domain from sourceto destination. A routing protocol is required to disseminatewavelengths’ and converters’ availability within the opticalnetwork domain. Kompella and Rekhter presented an Internetdraft in which they discussed the information that needs to beflooded by any routing protocol in support of GMPLS [7]. Inthat draft, a generic approach to handle networks comprisedof packet switch capable (PSC), time-division-multiplexingcapable (TDMC), lambda switch capable (LSC), and fiberswitch capable (FSC) equipment was presented, but the draftdid not address routing in networks comprised of LSC switcheswith any kind of wavelength conversion capabilities. We thinkthat the approach presented in [7] complicates the routingprotocol and makes it inefficient to handle LSC switches sinceit must handle the advertisements of equipment employing allpreviously mentioned switching technologies even though suchequipment might belong to different overlays as explained inSection II.

We believe that telecom networks employ an overlay archi-tecture and it is more efficient and feasible to design a routingprotocol that is specific to each of the employed overlays. Inthis case, despite the fact that each overlay would employ itsown routing protocol, it would be able to advertise more infor-mation that is specific to its intended overlay, resulting in moreefficient routing and better provisioning of network resources.In this section, we present an extension to the OSPF protocolthat addresses the routing problem faced by all-optical DWDMnetworks with sparse wavelength conversion resources. Eventhough the routing extension presented here is an overlay spe-cific one that pertains to all-optical DWDM transport networks[photonic overlay in Fig. 1(b)] regardless of their wavelengthconversion capabilities, a similar approach can be taken to de-sign routing protocols for other overlays.


Fig. 5. Wavelength availability opaque LSA.

Our proposed routing extension is based on three majorchanges to the OSPF routing protocol. The first change consistsof the introduction of two new link-state advertisements (LSAs)to advertise wavelengths’ and converters’ availabilities. Thesecond change involves modifications to the flooding policyof the OSPF protocol to make it more suitable for the dy-namic nature of all-optical DWDM networks. The last changeinvolves modifications to the route computation componentused in the original OSPF protocol that minimizes the cost ofthe lightpaths, while not hindering the blocking probability.The following two subsections address the first change, whileSections VI and VII address the changes to the flooding policyand the route computation engine, respectively.

A. Description of the New LSAs

The purpose of the introduced LSAs is to advertise the avail-ability of each wavelength per fiber and the number of wave-length conversion resources available within a switch. Our ex-tension is generic and it can handle all-optical DWDM networkscomposed of LSC switches regardless of their wavelength con-version capabilities. The introduced LSAs use the OSPF opaqueLSA option as a vehicle to advertising these parameters.

The OSPF opaque LSA option defined in [4] provides ageneralized mechanism for the OSPF protocol to carry ad-ditional information. An opaque LSA consists of a standardLSA header followed by 32-bit aligned application specificinformation field, which is divided into TLV tuples of type,length, and value. In addition to the fields defined in [6] and [7],the wavelength availability opaque LSA includes the followingfields (see Fig. 5).

• : This is a new sub-TLV that we intro-duce to the OSPF protocol to represent the link protectiontype. The value of the link protection type can be from 1to 6 to indicate the link protection type as explained in [7].

Fig. 6. Converter availability opaque LSA.

• : This is a new sub-TLV that we intro-duce to the OSPF protocol to represent all the Shared RiskLink Groups (SRLGs) to which the link belongs.

• : This is a new sub-TLV that we intro-duce to the OSPF protocol to represent the usage profile ofthe wavelengths carried on the link described in this LSA.

• Length of Mask: Number of bits used to represent thebandwidth mask.

• Wavelength Availability Mask: This field represents theusage profile of all wavelengths on a specific link. Thisfield can be extended or shortened as needed using thelength of mask field. If the value of the th bit of this filed isset to 1, then this indicates that the th wavelength of thespecified link is used. When the bit value is set to 0 thisindicates that the wavelength is free and it can be assignedto an incoming lightpath.

Fig. 6 depicts the structure of the converter-availabilityopaque LSA, where the difference between the total and usedwavelength converter fields represents the total number ofconverters that are not used of a given converter type within theswitch. This LSA contains the following fields.

• : We use the same concept used indefining the wavelength availability opaque LSA. Wedecided to assign a type value of 32 776 to the converteravailability TLV.

• Converter Type: Different types of wavelength convertersare commonly used in all-optical DWDM networks, sothis field is used to specify the type of wavelength conver-sion resources installed in the network. If multiple wave-length conversion types are installed in the network, mul-tiple fields can be used to specify the type and availabilityof these different types, as shown in Fig. 6. This field en-ables the protocol to convey important information aboutthe type of wavelength converters installed on a givenswitch allowing the route and wavelength assignment en-gine to distinguish between full-range wavenlength con-verters, limited-range wavelength-converters, and wave-length shifters.

• Total: The total number of wavelength converters of thespecified type that are installed on the switch.

• Used: The total number of wavelength converters of thespecified type that are currently in use.

In the TLVs and sub-TLVs presented above, we selected therange from 32 773 to 32 776 to represent the link protection type,Shared Risk Link Group (SRLG), wavelength availability, andconverter availability TLVs and sub-TLVs, respectively.


Fig. 7. Snapshot from our all-optical network simulation tool showing a typical 16-node long-haul all-optical DWDM network, where each link carries (8)wavelengths.

The wavelength and converter availability opaque LSAs in-troduced above are generic to handle all-optical DWDM net-works with different degrees of wavelength conversion capabil-ities. These LSAs provide a big advantage over the OSPF ex-tension presented in [7] which is not capable to handle LSC orFSC networks with different degrees of wavelength conversioncapabilities.

To illustrate the importance of the wavelength and converteravailability opaque LSAs that we introduced above, we per-formed a simulation study on the 16-node long-haul all-opticalDWDM network illustrated in Fig. 7. We generated randomcalls with exponentially distributed call holding and call inter-arrival times. The source and destination nodes of the generatedcalls were selected based on a uniform distribution. Also, theroute and wavelength assignments for the generated calls werebased on the least-cost and most contiguous route and wave-length assignment heuristics. The results of our study are de-picted in Figs. 8–10.

Fig. 8 compares the degree of blocking perceived when thewavelength and converter availability LSAs are exchanged be-tween the switches with that perceived when no wavelength andconverter availability LSAs are exchanged. It is clear from thisfigure that the exchange of wavelength and converter availabilityLSAs between the switches in the network can drastically de-crease the degree of blocking perceived in the network.

Fig. 9 depicts the average number of retries (or callcrankbacks) that need to be made before a call can be es-tablished when no wavelength or converter availability LSAsare exchanged. Call crankbacks can result in a drastic increasein the network offered load. These crankbacks or retries can

Fig. 8. Call blocking probability versus traffic load with and withoutadvertisements assuming 50% of wavelength conversion capability.

almost be eliminated when the switches exchange the wave-length and converter availability LSAs. This is possible sincethe exchanged LSAs inform the switches about the availabilityof the wavelength and converter resources throughout thenetwork. So, when a node runs the RWA algorithm for a calland informs the signaling mechanism to set up the end-to-endpath, the probability that the selected resources will not be inuse is very high.

Fig. 10 compares the degree of blocking perceived when thewavelength and converter availability LSAs are exchanged be-tween the switches with that perceived when no wavelength andconverter availability LSAs are exchanged in networks with dif-ferent degrees of wavelength conversion resources. We definethe degree of wavelength conversion here to be the ratio of the


Fig. 9. Average number of call retries versus traffic load with and withoutadvertisements assuming 50% of wavelength conversion capability.

Fig. 10. Call blocking probability versus degree of wavelength conversionwith and without advertisements.

number of wavelength conversion resources installed in the net-work to the total number of wavelengths in the network. Fig. 10shows that the wavelength and converter availability LSAs cangreatly enhance the blocking performance of the network espe-cially when the degree of wavelength conversion in the networkis low.

VI. MODIFIED OSPF ORIGINATION POLICY

The original OSPF standard defines a mechanism to origi-nate the router and network LSAs. Similarly, a mechanism needsto be defined to originate the wavelengths and converters avail-ability LSAs that we defined in Section V. It should be empha-sized here that the OSPF extensions in support of GMPLS pre-sented in [6] do not address this aspect of the protocol. In thissection, we propose two different origination policies. The firstone is simple and serves as our base policy for comparison. Thesecond policy is very efficient in handling the dynamic natureof all-optical DWDM networks with different degrees of wave-length conversion.

Several link-state update policies have been proposed in theliterature to minimize the routing protocol overhead neededto exchange the quality-of-service (QoS) parameters neededfor QoS routing. A link-state update policy determines when anode should originate link-state update messages and the con-tents of these updates. Apostolopoulos et al. [1] classified the

Fig. 11. IOA policy.

mechanisms used to trigger the link-state update messages intothreshold, class, and timer-based trigger policies. The tradeoffsbetween these mechanisms lie in the volume of link-stateupdate messages and the accuracy of the state informationavailable to the route computation engine. The exchange ofthe link-state information at a higher rate results in moreaccurate state information provided to the route computationengine. This means that the route computation engine will beable to provide lower call blocking probability at the expenseof a large volume of link-state traffic. Similarly, exchangingthe link-state information at a lower rate provides the routecomputation engine with less accurate information about thestate of the network. This means that the route computationengine will encounter a higher degree of call blocking but atthe same time, the network control plane is not overwhelmedwith large volume of link-state updates. However, the heuristicspresented in [1] have been applied to IP-based networks. Inthis section, we propose two link-state update mechanisms fordynamic all-optical DWDM networks with different degrees ofwavelength conversion.

A. Immediate Origination Approach (IOA)

Using this link-state update policy, each node should origi-nate wavelength-availability and converter-availability opaqueLSAs whenever a new router LSA is originated. Also, eachnode should originate a wavelength-availability opaque LSA foreach of its outgoing links whenever the wavelength availabilitymask of the link is changed. Moreover, each link should origi-nate a converter availability LSA whenever the usage profile ofthe wavelength conversion resources installed on the switch arechanged. Fig. 11 depicts this simple origination mechanism thatadvertises the wavelengths and converters availability LSAs assoon as the availability profiles of these resources change. Wecall this approach the IOA.

The strength of this approach stems from its simplicity as itcan be used in long-haul DWDM all-optical networks where thelightpath requests are static and do not change frequently. How-ever, in networks with dynamic lightpath requests, this approachcan result in a large volume of link-state updates since the avail-ability of the wavelength and conversion resources is constantlychanging. Since the lightpath requests presented to access andmetro-edge all-optical DWDM networks are usually dynamic,the immediate link-state update approach presented in this sec-tion is not efficient in handling such networks. The following


Fig. 12. Fuzzy rule base of proposed link-state origination policy.

section presents another link-state update mechanism that uti-lizes fuzzy logic. This mechanism can efficiently handle net-works with dynamic lightpath requests, as it is the case in mostaccess and metro-edge all-optical networks.

B. Fuzzy Origination Approach (FOA)

Since the availability of wavelength and wavelength-con-version resources of all-optical DWDM networks installed inmetro-edge and metro-core environments change frequently,a smart link-state update policy that minimizes the exchangeof link-state information, while not hindering the blockingprobability is needed. In order to satisfy this requirement, inthe following subsections we introduce the fuzzy logic-basedorigination approach (FOA).

1) Fuzzy Inference System: Our fuzzy-based link-state up-date policy is based on two simple rules, as shown in Fig. 12.The first rule causes the link-state information to be exchangedless often when the wavelength and wavelength-conversion re-sources installed in the network are lightly utilized. While thesecond rule causes the link-state information to be exchangedmore often when the wavelength or wavelength-conversion re-sources installed in the network are highly utilized.

The rationale behind these rules is very simple. In the firstcase, inaccuracies in the state information pertaining to theavailability of the wavelength and wavelength-conversionresources in the network do not increase the call blockingprobability because old information related to resources notutilized is still valid. In the second case, the network becomesmore utilized and, therefore, most of its resources experiencedchanges since the last update, augmenting the error in the stateinformation. Therefore, in order to avoid increasing the callblocking probability unnecessarily, state information must beadvertised more frequently. The net effect of our policy is thereduction of the volume of state information exchanged, whilenot increasing the call blocking probability.

The fuzzy interference system presented in Fig. 12 is basedon the linguistic approach that depends on linguistic variableswhose values are words or sentences in a natural or artificial lan-guage rather than numbers. Our fuzzy link-state update policyutilizes human expert experience to identify when the networkresources in terms of wavelengths and converters are consideredto be highly utilized or lightly utilized. However, since the impre-cision or fuzziness is inherent in human judgments, representingthe utilization of the different resources using linguistic vari-ables makes it easier and more flexible for the human operatorto specify the level of usage of the different network resources.Then, a fuzzy-inference rule base can be used to aggregate the

network resource utilizations in terms of wavelength and con-verter resources into a single value that specifies the waitingfactor to send the next link-state update message. The waitingfactor calculated using the fuzzy-inference rule base is an abso-lute number. This number should be multiplied by the averagecall interarrival time to calculate the actual waiting time betweentwo consecutive link-state updates. No link-state update needsto be originated when the calculated waiting time between up-dates expires before having a new link-state update.

The fuzzy link-state update policy presented in this sectioncan be easily extended or modified since it is based on commonsentences (rules). On the other hand, it is very difficult to rep-resent these rules using mathematical functions. But even if itis feasible to do that, it is definitely not easy to modify them asfrequently as required.

Even though the link-state update policy presented here ap-plies to all-optical DWDM networks with sparse wavelengthconversion capabilities, a similar approach can be used to de-sign a fuzzy-inference rule base that applies to IP-based net-works and advertise QoS parameters.

2) Membership Functions: The proposed FOA model uti-lizes two linguistic variables to represent the availability of thewavelength and converter resources in the optical network andone output linguistic variable to represent the waiting factorbetween two consecutive link-state updates. The membershipfunctions assigned to these variables are chosen as -functionsand -functions for the input linguistic variables and Gaussianfunctions for the output variable. The and membership func-tions are chosen for the input variables because they can be con-figured to provide fast as well as slow transitions from member-ship to nonmembership and vice versa (e.g., from high-degreeof utilization to a low-degree of utilization). The Gaussian mem-bership function is chosen for the output variable because it re-sults in smooth switching as the input linguistic variables changevalues. It is worth mentioning here that studies conducted onfuzzy systems have shown that the choice of membership func-tions does not drastically change the behavior of the system.

The and membership functions are specified by two pa-rameters and and the Gaussian membership function is spec-ified by two parameters and as follows:

Gaussian

Fig. 13 shows the general form of the resource availabilitymembership functions. Low membership function provides dif-ferent degrees of membership that range from full membershipwhen the resource utilization is lower than 10% to nonmember-ship when the resource utilization exceeds 70%. On the otherhand, High membership functions provides different degrees


Fig. 13. Resource availability input membership functions.

Fig. 14. Waiting factor output membership functions.

of membership that ranges from nonmembership when the re-source utilization is lower than 30% to full membership whenthe resource utilization exceeds 90%. A human operator caneasily tune the parameters involved in these membership func-tions.

Fig. 14 shows the general form of the waiting factor outputmembership functions. Low waiting factor means that thewaiting time between two consecutive link-state originationsis low resulting in more frequent updates. While high waitingfactor means that the waiting time between the link-state up-dates is longer resulting in less frequent exchange of link-stateupdate messages on the network control plane. The outputmembership functions have the following parameters:

Low Waiting Factor Gaussian

High Waiting Factor Gaussian

Fig. 15 provides an example that illustrates the computationof the update interval based on the fuzzy rule base. The rec-ommended rules try to maximize the waiting factor as longas the wavelength and converter resources in the network arenot highly utilized. Higher resource utilizations result in lowerwaiting factors. The waiting factor increases gradually as the re-source utilizations increase. This helps in minimizing the link-state update messages exchange on the optical network controlplane whenever that is possible. It is worth noticing that ourpolicy utilizes the min, max, min, max, and centroid methods

Fig. 15. Example of applying our fuzzy inference system rules to calculate theinterval between link-state updates.

for the fuzzy and, or, implication, aggregation, and defuzzifica-tion operators, respectively.

C. Performance Results

We carried out a performance study of the IOA and FOAapproaches. Fig. 16 plots the average number of update mes-sages exchanged under different traffic loads. The figure showsthat the average number of messages exchanged using our FOAstrategy is considerably smaller than the one needed by the IOALSA origination policy. In addition, in Fig. 17, we comparethe blocking probability of both schemes under different trafficloads and show that the FOA strategy does not increase theblocking probability. Based on these findings, we conclude that


Fig. 16. Number of link-state updates exchanged versus traffic load for theimmediate and fuzzy-based link-state origination policies.

Fig. 17. Call blocking probability versus traffic load for the immediate andfuzzy-based link-state origination policies.

our fuzzy-based LSA origination policy enables the routing pro-tocol to exchange less update messages without degrading theblocking performance.

VII. PROPOSED RWA STRATEGIES

Now, we apply fuzzy logic to routing in all-optical DWDMnetworks with sparse wavelength conversion capabilities. In theproposed approach, a fuzzy-inference rule base is used to as-sign a fuzzy cost to each path based on crisp metrics that reflectthe availability of resources within the network. The fuzzifiermodule takes these crisp inputs and generates fuzzy values thatcan be used by the fuzzy inference system. Next, the fuzzy in-ference system applies the rules available in its rule base to gen-erate a fuzzy cost for each of the possible paths. Then, the de-fuzzifier module takes the fuzzy cost and generates a crisp costfor each of the possible paths. Finally, the path selection modulecompares the costs of all the possible paths and selects the bestpossible route.

The proposed fuzzy-based model is shown in Fig. 18. Inthis model, the -shortest paths module is used to find thebest possible routes for the requested lightpath. At the sametime, the model monitors the availability of the wavelength andconverter resources within the network domain by accessingthe routing protocol link-state database (LSDB) that recordsthe wavelength and converter LSAs described in Section V.The wavelength assignment module employs a simple heuristicthat we developed called the most contiguous wavelengthassignment heuristic. In this heuristic, a set of wavelengths isassigned to the route in order to minimize the use of the wave-length converters. Our most contiguous wavelength assignment

heuristic works by choosing the wavelength that is most con-tiguous (avoiding wavelength conversion) and uses wavelengthconversion when the rest of the path cannot continue on thesame wavelength (wavelength is used).

The rest of this section is organized as follows. Section VII-Aintroduces our fuzzy-based route selection module, the designedrule base, and the membership functions. Section VII-B, intro-duces our most contiguous wavelength assignment heuristic.Finally, Section VII-C provides our simulation results com-paring the performance of our proposed fuzzy-based routeselection and most contiguous wavelength assignment heuristicwith some of the well known and used approaches presented inthe literature.

A. Fuzzy Route Selection Module

In all-optical DWDM networks, it may be too simplistic toemploy conventional routing strategies that are based on theevaluation of a single routing metric. In such networks, it iscrucial to employ routing algorithms that provide optical pathswith the lowest possible cost, while maintaining a low light-path blocking probability at the same time. Additionally, theserouting algorithms should avoid the usage of the wavelengthconversion resources installed within the optical network. Giventhese requirements and the complex tradeoffs between them, itis difficult to define a single routing metric for routing algo-rithms in such networks.

Therefore, a new routing paradigm that searches for accept-able routes for intended lightpaths, while satisfying their QoSrequirements is required for all-optical DWDM networks. Sucha paradigm will affect not only the QoS offered to the opticallightpaths but also the utilization of the network resources andthe blocking probability encountered in the optical network. Inthis section, we propose a fuzzy-based heuristic for the routingproblem in DWDM networks with sparse wavelength conver-sion capabilities. The proposed approach employs a fuzzy-in-ference rule base to assign a fuzzy cost to each path based on thecrisp metrics of the path, network links, and network resourceutilization (wavelength and converter resource utilizations).

1) Fuzzy Inference System: Routing algorithms are neededto select a set of links that need to be used to route the light-path through the optical network from its source to its desti-nation. The problem of assigning a route to an optical light-path based on two or more QoS parameters is an NP-completeproblem. In this section, we propose using fuzzy logic tech-niques to solve this problem. The challenge of this work is toroute lightpaths through the optical network over paths that sat-isfy the QoS requirements of the routed lightpaths, without hin-dering the blocking probability encountered throughout the op-tical network.

Our fuzzy-based route selection module is based on 12 simplerules, as shown in Fig. 19. The recommended rules try to assigna fuzzy cost for each path based on the QoS parameters of thatpath as well as on global network state information. When net-work bandwidth congestion level is high and network convertercongestion level is low, shorter paths are more preferred evenif they use more wavelength converter resources since these re-sources are not heavily utilized. In a similar manner, when net-work bandwidth congestion level is low and network converter


Fig. 18. Fuzzy-based routing model for all-optical DWDM networks with limited wavelength conversion capabilities.

congestion level is high, paths that use less wavelength converterresources are preferred even if they are longer in terms of theirnumber of hops since the network links are not highly utilized.This helps in assigning fuzzy costs to paths based on the net-work and path state information in such a way that conservesthe network resources when these resources are highly utilizedand at the same time provides lower cost paths when the net-work resources are not highly utilized.

The fuzzy route selection module presented in this sectioncan be easily extended or modified since it is based on commonsentences (rules). A similar approach can be used to design afuzzy-inference rule base that applies to IP-based networks toselect paths based on multiple QoS parameters.

2) Membership Functions: In our fuzzy route selectionmodule, membership functions are used in the antecedents andconsequentsof rules.Theproposedmodelutilizeseight linguisticvariables to represent the route QoS metrics and the network stateinformation. The model also has one output linguistic variablethat represents the overall fuzzy cost assigned to the path. Themembership functions assigned to these variables are chosen as-functions and -functions for the input linguistic variables and

Gaussian functions for the output variable.

In the following, we provide the general form and parametersof the eight input membership functions used in our fuzzy routeselection module.

• Hops-high: This membership function is -shaped (seeFig. 20), the values of its and parameters depend onthe spacing between the dispersion compensation mod-ules (DCMs), amplifiers, switching systems noise figures,and fiber lengths in the network. We suggest the followingstrategy to assign values for the and parameters.

When the optical signals exchanged between the dif-ferent source-destination pairs in the network have low-power levels or optical signal to noise ratios (OSNRs),the values assigned to the and parameters should be

low (e.g., and ). When the optical signalsexchanged between the different source-destination pairsin the network have high-power levels and OSNRs, thevalues assigned to the and parameters can be high (e.g.,

and ).• Cost-high: This membership function is -shaped, the

values of its and parameters depend on the range ofvalues used for the cost metric. Usually, the cost metricis a number between 0 and 255. In this case, we suggestthat the values of the and parameters be and

.• Network bandwidth congestion-high: This membership is

-shaped with parameters and . The valueof the parameter can be lowered to assign higher overallcost for connections that use more hops when the networkbandwidth congestion level is high. This helps penalizeconnections that use more hops when the network band-width conversion level is high.

• Network converter congestion-high: This membership is-shaped with parameters and . The value

of the parameter can be lowered to assign higher overallcost for connections that use more wavelength converterswhen the network converter congestion level is high. Thishelps penalize connections that use more wavelength con-version resources when the network converter conversionlevel is high.

• Diversity-low: This membership is -shaped (see Fig. 21);the value of the parameter can be increased in networkswith redundant links. This helps penalize connections thatuse unprotected paths, thus encouraging the connectionrequest to be routed over protected paths.

• Path bandwidth congestion-high: This membership func-tion is similar to the Network bandwidth congestion-highmembership function. Choosing the right value for theparameter of this function encourages connection to se-lect paths that are not congested.


Fig. 19. Fuzzy rule base of proposed route selection module.

Fig. 20. s-shaped cost-high membership function.

• Path converter congestion-high: This membership func-tion is similar to the Network converter congestion-highmembership function. Choosing the right value for theparameter of this function encourages connection to se-lect paths with less wavelength conversion congestion.

Fig. 21. z-shaped diversity-low membership function.

• Number of converters-high: This membership is -shaped;the value of the parameter can be lowered to assignhigher overall cost for connections that use more wave-length converters. This helps penalize connections that usemore wavelength conversion resources.


Fig. 22. Output membership functions used for fuzzy route selection.

Fig. 23. Example of applying our fuzzy inference system rules to calculate the overall path cost.

Fig. 22 shows the general form of the overall cost outputmembership functions. The output membership functions havethe following parameters:

Low Cost Gaussian

Low Hops Gaussian

Low Converters Gaussian

High Diversity Gaussian

Low Diversity Gaussian

High Converters Gaussian

High Hops Gaussian

High Cost Gaussian

The output membership functions used in our fuzzy route se-lection model are based on the Gaussian membership functionwith spacing of 100 units between the functions and .A human operator can adjust these parameters to calculate theoverall fuzzy cost of a path in a different way.

Fig. 23 provides an example that illustrates the computationof a fuzzy cost for each of the possible routes generated by the

-shortest paths module. The recommended rules try to assignfuzzy costs to paths based on the network and path state in-formation in such a way that conserves the network resourceswhen these resources are highly utilized and at the same timeprovides lower cost paths when the network resources are nothighly utilized. It is worth noticing that our policy utilizes theprobabilistic or, max, min, max, and centroid methods for thefuzzy and, or, implication, aggregation and defuzzification op-erators, respectively.


Fig. 24. Proposed most contiguous wavelength assignment heuristic.

B. Most Contiguous Wavelength Assignment Heuristic

The wavelength assignment problem has been studied exten-sively [14]. A large number of wavelength assignment schemeshave been proposed in the literature, as explained in Section III.

However, none of these wavelength assignment schemes ac-count for the scarcity of the wavelength conversion resourcesavailable in networks with sparse conversion capabilities. Forsuch networks, we propose a simple wavelength assignmentscheme that minimizes the use of wavelength conversion re-sources as much as possible. The rationale behind this is thatthe wavelength conversion resources are very scarce in such

networks and having a wavelength assignment technique thatconserves the usage of these resources is a critical requirementthat can drastically conserve the usage of these resources and,thus, enhance the network blocking performance significantly.Fig. 24 provides a high-level description of the proposed algo-rithm. It should be noticed here that the proposed algorithm con-serves wavelength conversion resources as much as possible.However, when a tie occurs between multiple wavelength as-signment options, any of the simple pack/spread wavelength as-signment schemes presented above can be used to break the tie.We suggest using the first-fit wavelength assignment scheme tobreak such ties because of the simplicity and good performance


Fig. 25. Comparison of the average path cost versus degree of wavelengthconversion of our fuzzy-routing and most contiguous wavelength assignmentheuristic with the least-hops first-fit and least-cost most-used heuristics.

of this scheme. Also, notice that the algorithm proposed doesnot guarantee that it will always find the wavelength assignmentwith the lowest possible number of wavelength converters. Ascheme that will always find the lowest number of wavelengthconverters can be computationally extensive and the schemeproposed here provides a good balance between simplicity andthe efficiency of the found solutions.

C. Performance Results

The performance of our proposed fuzzy-based routingheuristic has been compared with that of the shortest-pathrouting and first fit wavelength assignment approach. Thelightpath requests presented to the simulated network followa Poisson arrival process (i.e., the interarrival times are ex-ponentially distributed); the parameter of the arrival processdepends on the traffic load presented to the simulated network.The call holding time utilized in this simulation is also expo-nentially distributed with an average of 15 s. Our simulationtool generated one million lightpath requests to determine theblocking probability of the network. The source and destinationof the generated lightpath requests are selected with uniformprobability.

Figs. 25–27 compare the performance of our proposedfuzzy-routing approach when combined with our proposedmost contiguous wavelength assignment heuristic. It is clearthat our routing and wavelength assignment approach resultedin better blocking performance and better usage of the wave-length conversion resources, while not compromising the cost(or quality of service) of the selected lightpaths.

VIII. CONCLUSION AND FUTURE WORK

In this paper, we present a complete framework to handle thestatic and dynamic lightpath establishment problems in all-op-tical DWDM network with sparse wavelength conversion capa-bilities. For the static lightpath establishment, we present an ILPformulation for the RWA problem that applies to networks withdifferent degrees of wavelength conversion capabilities. We alsopresent a pruning strategy that helps to reduce the number of

Fig. 26. Comparison of the average number of converters versus degree ofwavelength conversion of our fuzzy-routing and most contiguous wavelengthassignment heuristic with the least-hops first-fit and least-cost most-usedheuristics.

Fig. 27. Comparison of the call blocking probability versus degree ofwavelength conversion of our fuzzy-routing and most contiguous wavelengthassignment heuristic with the least-hops first-fit and least-cost most-usedheuristics.

variables and constraints of the ILP formulation. We also presentan extension to the OSPF protocol in terms of two new opaqueLSAs to convey the availability of the wavelength and wave-length-conversion resources within the network. Two new link-state origination policies are also introduced and their perfor-mance is compared. Finally, we present a new wavelength as-signment heuristic called the most contiguous wavelength as-signment heuristic and a new fuzzy-based route computation en-gine targeted for all-optical DWDM networks. The performanceof our route computation engine is also compared with otherheuristics found in the literature and we show that our approachconserves wavelength-conversion resources and finds lower costpaths without hindering the call blocking probability.

In the future, we will simulate and tune our routing exten-sion, origination policies, wavelength-assignment and routingheuristics for optical burst switching (OBS) and optical packetswitching (OPS) networks.


REFERENCES

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versus wavelength converter placement in all-optical networks,” IEEEOpt. Commun., vol. 1, pp. S22–S28, Aug. 2003.

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Ala I. Al-Fuqaha (S’00–M’04) is a Senior Memberof Technical Staff at LAMBDA Optical Systems,Reston, VA, where he works on the design anddevelopment of embedded routing protocols andnetwork management systems for all-optical trans-port networks. Before joining LAMBDA, he was aSoftware Engineer with Sprint TelecommunicationsCorporation, where he worked on different projectsas part of the architecture team to design and developsoftware to manage Sprint’s core network (NortelDMS-250/300, ATM, SS7). His research interests

include high-speed computer and telecommunication networks, optical trans-port networks, wireless networks, network security, network management,embedded software, expert systems, distributed processing, simulation mod-eling, and computer architecture.

Ghulam M. Chaudhry (M’85–SM’98) receivedthe M.S. and Ph.D. degrees in computer engineeringfrom Wayne State University, Detroit, MI, in 1985and 1989, respectively.

Currently, he is an Associated Professor inthe Department of Electrical and Computer En-gineering, University of Missouri–Kansas City.His teaching/research interests include computerarchitectures, parallel and distributed systems,Verilog HDL, computer network management, andVLSI. He has published extensively in national and

international journals and conferences. He has served on the steering/programcommittees of several international conferences and on the editorial boards ofthe journals.

Prof. Chaudhry has served as General Chair for the International Conferenceon Parallel and Distributed Computing Systems in 2002. He is a member ofISCA, the Association for Computing Machinery (ACM), and IASTED.

Mohsen Guizani (S’83–M’90–SM’98) received theB.S. (with distinction) and M.S. degrees in electricalengineering, and the M.S. and Ph.D. degrees incomputer engineering from Syracuse University,Syracuse, NY, in 1984, 1986, 1987, and 1990,respectively.

Currently, he is a Professor and the Chair of theComputer Science Department, Western MichiganUniversity, Kalamazoo. His research interests in-clude computer networks, wireless communicationsand computing, and optical networking. His research

has been supported by Sprint, Telcordia, the U.S. Navy, and Boeing, to namea few.

Dr. Guizani is a member of the IEEE Communications Society, the IEEEComputer Society, the American Society for Engineering Education (ASEE),the Association for Computing Machinery (ACM), the Optical Society ofAmerica (OSA), SCS, and Tau Beta Pi.

Miguel A. Labrador (M’96–SM’04) receivedthe M.S. degree in telecommunications and thePh.D. degree in information science with concen-tration in telecommunications from the Universityof Pittsburgh, Pittsburgh, PA, in 1994 and 2000,respectively.

Before joining the Department of Computer Sci-ence and Engineering, University of South Florida,Tampa, as an Assistant Professor in 2001, he waswith Telcordia Technologies, Inc., as a Consultantin the Broadband Networking Group of the Pro-

fessional Services Business Unit. He is currently on the Editorial Board ofComputer Communications. His research interests are in the areas of transportlayer protocols, active queue management, and optical networking.

Dr. Labrador has served as Technical Program Committee Member of manyIEEE conferences, and was the former Secretary of the IEEE Technical Com-mittee on Computer Communications.

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