Multi-Domain Routing Techniques with TopologyAggregation in ASON Networks

Guido Maier, Chiara Busca, Achille PattavinaDipartimento di Elettronica e Informazione

Politecnico di Milano, Piazza L. da Vinci, 3220133 Milano, Italy

Email: {maier,pattavina}@elet.polimi.it

Abstract—Thanks to the development of the automatic controlplane (ASON and GMPLS standards) optical transport networksare able to provide bandwidth on demand service quicklyand efficiently. But for this service to be really interesting tousers the capability of seamlessly operating the network acrossmultiple administrative domains should be developed. Multi-domain routing in ASON is a new challenging topic, especiallyif the target is a scalable solution. The paper investigates thistopic and in particular it evaluates the effectiveness of theapplication of aggregation topology methods to the representationof network domains. These methods, initially devised for ATMnetworks, can be extended to ASON/GMPLS, as recommendedby the Optical Interworking Forum (OIF). Topology aggregationlimits the amount of topology information distributed throughoutthe network and the bandwidth occupation for control-planesignalling, improving scalability. On the other hand, concealingintra-domain topology details to other domains may limit theeffectiveness of inter-domain routing. We analyze the behaviorof the three topology-aggregation methods proposed by OIF(Simple Node, Full Mesh and Symmetric Star) comparingblocking probability in a multi-domain optical network underdynamic bandwidth-on-demand traffic. We further propose a newtopology-aggregation scheme (Hybrid) with improved scalabilityand evaluate its performance in the case-study network comparedto the other known schemes.

I. INTRODUCTION

In the last few years, the ever increasing bandwidth avail-ability of optical networks and the development of advancedapplications have fostered the development of the so calledBandwidth on Demand (BoD) service: the user can ask forhigh bitrate connections across the network for a limitedamount of time according to its specific needs. Engagedresources are then released when they are not needed anymoreand can be assigned to other connections. Given the high levelof integration worldwide between users of telecommunicationservices brought-by by Internet, it is clear that BoD is effectiveprovided that several different network domains are ableto cooperate to setup connections between geographically-far end-points. Connections spanning over several domainsare feasible if interoperability (or horizontal integration) isensured between different dominans and equipment.

The deployment of optical networks based on Dense Wave-length Division Multiplexing (DWDM) has surely been anenabling factor, but it is not sufficient for BoD. The Optical

This work has been partially supported by the European Commission underthe projects MUPBED and EuroFGI.

Transport Network (OTN) layer, standardized some years agoand currently supported by the majority of optical-networkoperators, guarantees interoperability. However OTN inheritsseveral paradigms typical of SDH/SONET protocols fromwhich OTN evolved. In particular, it is a static architecture,relying upon a centralized management plane to exploit op-erations such as connection establishing, routing, connectiontear-down, etc. This feature is a weakness when providing BoDservice because of the high quantity of manual intervention ofoperators that is needed to reconfigure the network through themanagement plane. So connection setup and release delays arehigh, sometimes even in the order of hours or days. This makesOTN inadequate to the future telecommunication scenario.

BoD makes optical-network traffic dynamic. A fast responseto user requests becomes an important competitive factor foroperators to reduce time-to-market. Moreover, as the numberof setup and release operations per time-unit increases, relyingentirely on the management plane would result in a tremen-dous increase in the Operational Expenditure (OPEX), due tothe need of expanding personnel in the centralized networkoperating center to activate network reconfigurations and toupdate databases.

The above reasons forced the development of a technologyable to establish, maintain and release the connections in afast and automatic way. The International TelecommunicationUnit - Standardization Sector (ITU-T) has thus developed theAutomatically Switched Optical Network (ASON) standard[1]. ASON is, in fact, a protocol-independent architecture, thatmakes the network transparent to the different technologiesimplemented and allows to manage the transport layer viaan automatic and dynamic fully-distributed control plane.Another standard that possesses similar features and can beconsidered complementary to ASON, is the Generalized MultiProtocol Label Switching (GMPLS) suite [2], extension ofthe well known MPLS protocol developed by the InternetEngineering Task Force (IETF).

ASON/GMPLS in principle guarantees horizontal integra-tion in a multi-domain network. Many aspects regarding howinteractions should practically occur between a set of domainsthat are controlled by different operators are still open and notwell defined. While in IP networks rules of communicationbetween the autonomous systems are set by well establishedprotocols (e.g. BGP), the same is not true for ASON. The gap

X. Masip, S.Sánchez, J.Solé, A.Jukan (Eds.): ONDM 2008© IFIP 2008

147

is being filled by the definition of standard interfaces betweennetwork domains (External Node Network Interface - E-NNI),in parallel with the definition of end-point interfaces (UserNetwork Interface - UNI) of the transport network. This workis mainly carried out by the Optical Internetworking Forum(OIF), an organization of operators and equipment vendors.Among the many aspects still to be clarified, representation ofintra-domain topology in the other domains is very relevant.Investigating this aspect is the aim of the paper.

In section II a general description of the topology aggrega-tion problem is provided. In section III the static and dynamicsimulation results for different topology-aggregation methodsare shown and discussed.

II. TOPOLOGY AGGREGATION

Since in the ASON architecture the control plane is dis-tributed, each node participates to connection setup and rout-ing. The control-plane component responsible of routing ineach node is called Routing Controller (RC). Within a singlerouting domain (routing area, according to ASON terminol-ogy), the RC must keep an accurate (complete) vision ofthe whole domain that is constantly updated by combiningthe topological information and link state messages receivedby the RCs of the other nodes. The node itself has then toredistribute relevant information (e.g. variations) of its owntopology view to the other RCs. Whenever a new connectionis setup in the domain, all RCs are updated in a synchronizedway.

The distributed-routing approach applied within a domaincan not be extended the same in a multi-domain networkbecause of the problems of scalability that arise with a veryextensive and complex topology. It’s obvious that the increaseof the quantity of distributed information and the frequency ofupdates increases the overhead of the entire network signalingad thus the quantity of bandwidth and computational powerconsumed. Moreover, in a multi-domain environment theremay be strong commercial reasons that refrain a domainadministrator to disclose all the details of its intra-domaintopology: the topology of a network itself is usually regardedas a sensitive information to disclose to possible competitors.

The solution to the above problems is to setup a hierarchicalpartition of the network and a set of possible policies bywhich information is distributed among the levels of thehierarchy. This concept is the basis of the ASON routingarchitecture [3], [4]. An ASON network is partitioned intoRouting Areas (RAs). Typically, a RA covers an administrativedomain. The synchronized “ensemble” of routing controllersof an RA behaves as a single abstract entity called RoutingPerformer (RP), maintaining detailed intra-area routing infor-mation and a synthetic and summarized view of the topologyof the other RAs. For inter-area (inter-domain) routing, ASONRecommendations describe the kind of routing informationexchanged by the RPs and how path computation is distributedbetween the RPs across the network. A network elementcan be topologically represented in terms of link-state (i.e.associated to state attributes such as: administrative weight,

occupancy, free bandwidth, delay, etc.) or reachability (i.e.purely topological information). Path selection can be source-based or step-by-step (i.e each crossed area computes routingfor the portion of the path falling within the area).

Once a routing architecture is defined, the next step is totake advantage of the RA hierarchy to define policies for inter-area (inter-domain) information release. To make the networkscalable in the overhead of topological signaling two mainmethods can be adopted:

• quantity reduction: reduce both number and dimensionsof routing messages limiting accuracy of representationof parts of topology that are less essential;

• frequency reduction: generate and distribute routing mes-sages less frequently.

In both methods care has to be taken not to degrade inter-domain routing performance too much. In this paper weconsider quantity reduction only, while frequency reductionis currently being investigated in another work under develop-ment.

Topology aggregation is one of the main techniques toimplement quantity reduction: the basic idea is to substitutethe actual intra-domain topology with a regular topologyof “virtual” (or equivalent) links interconnecting the bordernodes of the domain. The regular topology to be adopted ischosen among a limited set of possibilities. The advantages ofthis method are the following: a) the aggregation operationis systematic: once a specific virtual topology is selected,aggregation may be carried out with the same criteria in all thedomains; b) mapping of real to virtual topology is quite easyto implement according to simple rules; c) internal topologicaldetails are hidden, but all border nodes or equivalently all inter-domain links of the network are revealed in full details to alldomains of the network: this is important to be able to performsource inter-domain routing at list in a loose way.

Topology aggregation has been initially suggested at theend of the 1990s for the development of the Private Network-Network Interface (PNNI) of ATM networks [5]–[10]. Af-terwards several studies [11] have extended such techniqueto IP networks. Recently some workgroups and standardiza-tion boards have shown interest towards the application oftopology aggregation in new network environments such asASON/GMPLS [12]–[14]. Specifically, the OIF, in a recentImplementation Agreement [15] and in a workshop [16], hasproposed the technique for ASON. According to OIF vision,routing information is propagated between different ASONdomains via standard E-NNI interfaces. OIF has in particularproposed a set of three regular topologies to be adoptedin topology aggregation: Abstract Node, Abstract Link andPseudo-node. Virtual topology complexity, as well as amountof propagated information, increases form Abstract Node toPseudo-node.

In spite of their different names, the above mentionedtopologies suggested by OIF coincide with three of the ag-gregation methods we are investigating in this work: namelyand respectively, Simple Node, Full Mesh and Symmetric Star.

148

A. Measurement of complexity (TTE parameter)

In order to evaluate the complexity of the aggregationschemes, we adopt a parameter named “Total number ofTopology Elements” (TTE), introduced for the first timein Ref. [18]. TTE counts the topology elements stored inthe routing performer databases of the routing areas of thenetwork. For each aggregation method m, in a multidomainnetwork composed of D RAs (assumed coincident with thedomains), TTE is defined by the equation

TTEm =

D∑

k=1

[N(k,m) + Uk + 3L(k,m)] · RCk

k indicates an RA comprising RCk routing controllers (nodes).k also identifies the corresponding RP. In the network rep-resentation of RP k there are: Uk destination addresses andN(k,m) topological nodes associated to reachability informa-tion; L(k,m) links associated to link-state information. Thenumber of destination addresses propagated by each areacorresponds to the number of UNIs located in the area. A link-state object in the RP topology is assumed to be equivalent to3 topology elements (TEs), while a generic node or an UNIaccounts for 1 TE.

TTE is effective in providing a simple mean to numericallycompare aggregation-method complexity once the physicaltopology is given. In particular, TTE can be viewed as thesum of three components: TTEm = ETTEm + ITTE + UTTE.We are interested in the component ETTEm that is due tothe representation in each domain of the links and nodes ofother domains. The other components ITTE and UTTE aredue to representations of the intra-domain topology in eachdomain and of the UNI addresses of the whole network,respectively. These two components are independent of thetopology-aggregation method adopted in the network, sincewe assume that intra-domain topology is never aggregated andthat all UNIs are always notified inter-domain.

To specify ETTEm for each method m, let us furtherintroduce the following symbols. The overall multi-domainnetwork has N nodes and L links, of which LI and LE

are intra- and inter-domain links, respectively. Each domaink is characterized by: number of internal links lk, numberof internal nodes nk, number of border nodes bk, number ofoutgoing inter-domain links ek (i.e. for which the source nodeis a border node of the domain). All links are assumed to beunidirectional. Finally, we assume that each node of the net-work hosts a routing controller, and thus: RCk = nk+bk = qk.

B. Topology aggregation methods

Let us now introduce the aggregation methods according tothe different regular topologies we are referring to. First wepresent the case of no aggregation (Source Routing - Link-stateAll) that will be used as benchmark in comparative analysis.Then we introduce the three homogeneous-aggregation cases(Simple Node, Full Mesh and Symmetric Star), correspondingto known cases recommended by OIF standardization. Finallywe propose a new non-homogeneous-aggregation technique:(Hybrid).

1) Complete topology: Source Routing - Link-state All(SRC-LA): This scenario is presented for comparison pur-poses. Each domain advertises to the other domains of thenetwork its actual intra-domain topology in full details, with-out applying any aggregation. All the physical links arerepresented by full link-state information. In this case:

ETTESRC−LA =∑

k

∑

h�=k

(3lh + qh + 3eh) · qk

In this and in the other following equations sums are extendedfrom k = 1 (h = 1) to k = D (h = D). In practice the multi-domain network, from a control-plane point of view, behavesas a large single domain, except for the fact that connectionshaving both end-points in the same domain are constrainedto be routed on resources belonging to that domain. Thecomplexity of routing information that each RC of a domainhas to store, in terms of ETTE, is O (3L + N). It is expectedthat inter-domain routing with this case behaves the best, buta lot of signalling overhead is needed.

2) Simple Node (SN): This is the simplest aggregationmethod. Each domain is collapsed in a single virtual nodeconnected to the other domains by the inter-domain links. Thisscheme does not provide any information about the domaininternal resources and for this reason it implies low inter-domain signalling overhead. We can write:

ETTESN =∑

k

∑

h�=k

(1 + 3eh) · qk

The complexity of ETTE per RC is O (D + 3LE). It isexpected that SN aggregation will substantially reduce therouting performance of the network.

3) Full Mesh (FM): The Full Mesh method allows us tosynthesize the domain internal topology. Every domain isrepresented by a complete graph of virtual links between itsborder nodes. We have:

ETTEFM =∑

k

∑

h�=k

[bh + 3bh(bh − 1) + 3eh] · qk

The complexity of ETTE per RC isO(

B + 3∑

k(bk)2 + 3LE

)

, where B is the total number ofborder nodes in the overall multi-domain network.

It is very important to decide which specific link-aggregation criterion has to be adopted to create the vir-tual links and their link-state parameters. Previously pre-sented works have proposed many different full-mesh link-aggregation methods. Every virtual link could represent, forexample, the shortest path between two border nodes com-puted according to a specific routing metric, the maximumbandwidth path, the minimum delay path (in case also QoSattributes were associated to the domain links), etc. In thiswork we assume the minimum-cost path, computed accordingto the link administrative weights, as mapping criterion.

It is expected that FM achieves routing performance similarto SRC-LA. However, as complexity strictly depends on thenumber of border nodes, FM may not be convenient in largemulti-domain networks with a high number of inter-domainlinks.

149

4) Symmetric Star (SS): This method can be used tofurther synthesize the topology starting from the Full Meshrepresentation. It provides a compromise between FM andSN. In the Symmetric Star method all the border nodes ofeach domain are connected to a central virtual node (called“nucleus”) by virtual links called “spokes”.

ETTESS =∑

k

∑

h�=k

[bh + 1 + 3bh + 3eh] · qk

The complexity of ETTE per RC is O (D + 4B + 3LE),where B is the total number of border nodes in the overallmulti-domain network.

It is important to clarify how routing weights are assigned tospokes. In each domain k, the weights p

in/outSS(i,k) assigned to the

two spokes (one incoming and one outgoing) connected to thethe i-th border node derive from the set of incoming/outgoingvirtual links connected to the same border node in the FM caseand they are calculated rounding-off the following equationresult:

pin/outSS(i,k) =

1

2

∑bk−1j=1 p

in/outFM(i,j,k)

bk − 1

,

where pin/outFM(i,j,k) is the weight of the incoming/outgoing FM

virtual link connecting the border nodes i and j in the FullMesh representation of domain k. The halved value is dueto the fact that any path crossing an SS aggregated domainengages two virtual links, while only one link in the FM case.

We have decided to give infinite weight (correspondingto the unavailability of paths) to a spoke only when ALLthe corresponding Full Mesh links have infinite weight. Analternative is to assign infinite weight to a spoke when ATLEAST ONE corresponding FM link has infinite weight. Thisoption has shown to lead to very poor routing and it has thusbeen disregarded in this study.

5) Hybrid (H): We introduce the new Hybrid aggregationmethod that blends together the use of FM and SS. This is anon-homogeneous approach: each domain aggregates internaltopology according to the FM method when the number ofborder nodes is low compared to the total number of nodes;otherwise, the domain adopts the SS aggregation method.ETTEH is given by

ETTEH =∑

k

∑

h�=k

{sh[bh + 1 + 3bh + 3eh] +

+ (1 − sh)[bh + 3bh(bh − 1) + 3eh]} · qk

where: sh = 1 if lh < bh(bh − 1); otherwise, sh = 0. Inwords, a domain is represented by FM aggregation only whenthe number of its internal physical links is higher than thenumber of virtual links (bh(bh − 1)) that would be impliedby the full-mesh representation; otherwise, SS is adopted. Thehybrid method guarantees that the virtual links are always lessthan the internal physical links, ensuring that the representationis an actual aggregation.

III. PERFORMANCE EVALUATION

A. Simulation Model

This work is an extension of previous studies carried outon ASON multi-domain routing in the framework of theEuropean IST-Project MUPBED [17], [18]. For this reason wehave analyzed in this paper the same case-study multi-domainnetwork that has been used in our previous MUPBED studies.

Simulations have been carried out using the network topol-ogy represented in Fig.1: it is composed by a simplifiedversion of the GEANT2 European research network intercon-nected to simplified versions of the 5 National Research andEducation Networks (NRENs) participating to the MUPBEDproject. Each one of these 6 subnets is assumed to be a domain(an ASON RA). Each NREN is interconnected to GEANT2by two ENNIs and to other two NRENs via ENNIs locatedon cross-border links1. In the whole network, low-capacity(16-wavelength) and high-capacity (64-wavelength) links areassigned administrative weight 4 and 1, respectively.

The network supports connection requests for unidirectionallightpaths from a source to a destination UNI. Only somenodes (“edge nodes”) of the NRENs are equipped with UNIs.GEANT2 does not have UNIs. Every connection requestis routed according to Source-Routing loose path-selection,based on the minimum-administrative-cost path. Firstly thesource node (which has a complete view of its own domainand an aggregated view of the rest of the network) determinesthe complete path inside its domain and the domain sequenceto reach the destination. After this first step, the ingress nodesof all the transit domains in the source-computed sequencechoose proper intra-domain routes. In this way the completesource-destination path is determined.

DFN

910 11

1413

15

17

1618

19

20 21

2326

22

2524

2728

29

3132

30

3433

3540

36

39

37

3842

4345

44

41

46

1

23

4

56

8

7

12

REDIris

GARR

Pionier

NORDUnet

GEANT2

Edge node

Core nodeLow capacityHigh capacity

Fig. 1. Test network topology, GEANT2+NRENs.

B. Experimental Results

In order to compare the topology-abstraction procedures wehave considered both static and dynamic traffic conditions.A first static-traffic analysis has been ran, in which we have

1Not all of these links do exist in reality today, though it is a general trendthat more and more NRENs plan to operate cross-border fibers in future. Forthis study, cross-border links have been added in any case as displayed withthe purpose of generating a mesh of RAs.

150

assumed that a permanent lightpath-connection is set up foreach UNI-pair. We have measured the Average ConnectionCost (ACC) attained by each aggregation scheme. ACC is thetotal administrative cost of all the permanent connections (sumof the costs of crossed links) divided by the number of connec-tions (number of UNI-pairs): better performance correspondsto lower ACC. Fig.2 correlates TTE to ACC values for eachtopology aggregation scheme in our test network. The SRC-LA scheme naturally shows the best performances in terms ofcost, much better than all the aggregated schemes.

Fig. 2. TTE-ACC correlation for the topology-aggregation schemes.

The aggregated schemes display a similar ACC, decreasingin the following order: SN, SS, H and FM. It must be notedthat the FM method displays a clear performance advantageover the other aggregations, but results into a TTE higher thanSRC-LA.

Dynamic traffic simulations have then been carried out byconsidering connection requests for unidirectional lightpaths.Request events, generated by a Poissonian arrival process, areevenly distributed by randomly selecting source and destina-tion UNIs. The session length of a successful connection isa random variable with exponential distribution. The averageoffered load Ao (in Erlang) is given as input. A backgroundAo/2-load intra-domain dynamic traffic is added on GEANT2to simulate the traffic of other European NRENs not includedin our topology.

Fig. 3 represents the blocking probability Π for the differenttopology-aggregation methods as a function of the offeredload. Confidence of the simulations is checked on Π: theconfidence level is fixed at 95% for all simulations, while theconfidence interval varies from 1% of the average value of Πfor the highest value of Ao to the 10% for the lowest Ao.

The graph in Fig. 4 displays the same results in termsof difference ∆Π between the value Π of each topologyaggregation method and the corresponding Π of the non-aggregated SRC-LA case, for every value of offered load Ao.

Both the figures show that the SN aggregation is the worstperforming one in all conditions. The diagram in Fig. 4 allowsus to see that for high traffic intensity (Ao > 30 Erl) FM

Fig. 3. Blocking probability of the aggregation schemes.

Fig. 4. Blocking probability variation of the aggregation schemes, assumingSRC-LA as baseline reference.

performs almost as SRC-LA, while the other aggregationssuffer higher blocking-probability levels. This is confirmed byFig. 5 plotting the dynamic channel-occupation factor ρCH ,defined as the ratio between the average number of occupiedWDM channels over the total number of WDM channelsdeployed in the network. In heavy-load conditions only FM isable to achieve the same occupancy that we achieve withoutaggregation: for the other more scalable aggregation policiesthe loss of topology information decreases the capability ofusing network resources.

For Ao > 30 Erl SS and H methods result to be intermediatebetween FM and SN as expected. In low-traffic conditions(Ao ≤ 30 Erl) SS and H blocking probability is very similar tothe FM. SS and H even seems to have a slightly-better behaviorthan FM. Moreover, for very high loads (Ao > 60 Erl) we haveregistered cases in which FM seems to perform slightly betterthan SRC-LA. These two latter unexpected phenomena need tobe further investigated, as in the simulations carried out resultsare so close that the related confidence intervals overlap andthus the results obtained may not be very significant.

It is interesting to evaluate the effect of topology aggregationon the effectiveness of the routing algorithm. As mentioned

151

Fig. 5. Dynamic channel occupation.

above, the objective of routing in this case-study is to minimizethe administrative cost of each connection. Fig. 6 representsthe average inter-domain administrative cost wINTER onlyof the inter-domain connections as a function of the offeredload and for each topology-aggregation method. Two differentbehaviors clearly appear. For low traffic-loads and for all theaggregation methods the cost does not decrease with load asquickly as it does in the non-aggregation case. This “penalty”introduced by the aggregation is clearly related to the lack oftopology information, that prevents the routing algorithm tofind the shortest paths even when they are available. For hightraffic loads, instead, curves of all the aggregation methods,except FM, result “clamped” to an asymptotic value. This indi-cates that blocking events hit with more intensity connectionsbetween far-away end-points: relatively-short connections arethe only ones that can be setup successfully with SN, SS andH under heavy loads.

The newly-proposed hybrid aggregation H behaves in allconditions very similarly to SS. This is also confirmed inthe static case (see Fig. 2). In the particular case-studymultidomain network we have considered hybrid aggregationis not very effective, most probably because by applying the Hrule, the only domain that is propagated as a symmetric star isGEANT2, which is also the most important from a topologicalpoint of view. It is however expected that the Hybrid methodcan in general be helpful in increasing scalability.

IV. CONCLUSION

The effectiveness of different topology aggregation methodsin multi-domain ASON network has been tested. The methodspresented in this article have been recently signalled by IETFand OIF as a future development for the ASON/GMPLSarchitecture. The known SN, FM and SS and the newly-proposed H schemes have shown different features that haveunderlined the existing trade-off, however quite complicated,between topology information reduction and inter-domainrouting performance.

Fig. 6. Average cost per inter-domain connection.

REFERENCES

[1] ITU-T International Communication Union, Architecture for the Auto-matic Switched Optical Network. 2000. G.8080.

[2] E. Mannie, “Generalized Multi-Protocol Label Switching (GMPLS)Architecture,” RFC 3945, 2004.

[3] ITU-T International Communication Union, Architecture and Require-ments for Routing in ASON. 2002. G.7715.

[4] ITU-T International Communication Union, ASON Routing Architectureand Requirements for Link State protocols. 2003. G.7715.1.

[5] T. Korkmaz and M. Krunz, “Source oriented Topology Aggregationwith Multiple QoS Parameters in Hierarchical Networks,” IEEE/ACMTransactions on Modeling and Computer Simulation, vol. 10, pp. 295–325, Oct. 2000.

[6] Y. Tang, S.Chen, and Y. Ling, “State Aggregation for Large NetworkDomains,” Computer Communications, vol. 30, pp. 873–885, Feb. 2007.

[7] K. Lui, K. Nahrstedt, and S. Chen, “Routing with Topology Aggregationin Delay-Bandwidth Sensitive Networks,” IEEE/ACM Transactions onNetworking, vol. 12, pp. 17–29, Feb. 2004.

[8] L. Guo and I. Matta, “On State Aggregation for Scalable QoS Routing,”Proceedings, IEEE ATM Workshop ’98, pp. 306–314, May 1998.

[9] F. Hao and E. Zegura, “On Scalable QoS Routing: Performance Eval-uation of Topology Aggregation,” Proceedings, IEEE INFOCOM ’00,vol. 1, pp. 147–156, March 2000.

[10] B. Awerbuch and Y. Shavitt, “Topology Aggregation for DirectedGraphs,” IEEE/ACM Transactions on Networking, vol. 9, Feb. 2001.

[11] M. Yannuzzi et al., “A Combined Intra-Domain and Inter-Domain QOSRouting Model for Optical Networks,” Conference on Optical NetworkDesign and Modeling, pp. 197–203, Feb. 2005.

[12] Q. Liu et al., “Hierarchical Routing in Multi-Domain Optical Networks,”Computer Communications, vol. 30, pp. 122–131, 2006.

[13] E. Marin-Tordera et al., “A Hierarchical Routing Approach for OpticalTransport Networks,” Computer Networks, vol. 50, pp. 251–267, Feb.2006.

[14] L. Lei and Y. Ji, “A Spanning Tree-based QoS Aggregation in Hierar-chical ASON,” IEEE Communications Letters, vol. 9, May 2005.

[15] OIF Optical Internetworking Forum, External Network-Network Inter-face (E-NNI) OSPF-based Routing - 1.0 (Intra-Carrier) ImplementationAgreement. Jan. 2007. OIF Optical Interworking Forum OIF-ENNI-OSPF-01.0.

[16] H. Martin Foısel, “ASON/GMPLS Optical Control Plane Tutorial.”MUPBED Workshop at TNC 2007, Copenhagen, www.oiforum.com,May 2007.

[17] MUPBED Multi-Partner European Test Beds for Research Networking,www.ist-mupbed.eu

[18] G. Maier, F. Mizzotti, and A. Pattavina, “Multi-Domain Routing Tech-niques in ASON Networks,” Proceedings, ECOC ’07. 33rd EuropeanConference and Exhibition on Optical Communication, Sept. 2007.

152