Role of Optical Network Virtualization inCloud Computing [Invited]Shuping Peng, Reza Nejabati, and Dimitra Simeonidou

Abstract—New and emerging Internet applications areincreasingly becoming high-performance and network-based, relying on optical network and cloud computingservices. Due to the accelerated evolution of these applica-tions, the flexibility and efficiency of the underlying opticalnetwork infrastructure as well as the cloud computing in-frastructure [i.e., data centers (DCs)] become more andmore crucial. In order to achieve the required flexibilityand efficiency, coordinated provisioning of DCs and opticalnetwork interconnecting DCs is essential. In this paper,we address the role of high-performance dynamic opticalnetworks in cloud computing environments. A DC as a ser-vice architecture for future cloud computing is proposed.Central to the proposed architecture is the coordinatedvirtualization of optical network and IT resources of dis-tributed DCs, enabling the composition of virtual infra-structures (VIs). During the composition process of themultiple coexisting but isolated VIs, the unique character-istics of optical networks (e.g., optical layer constraints andimpairments) are addressed and taken into account. Theproposed VI composition algorithms are evaluated overvarious network topologies and scenarios. The resultsprovide a set of guidelines for the optical network andDC infrastructure providers to be able to effectively andoptimally provision VI services to users and satisfy theirrequirements.

Index Terms—Data center as a service (DCaaS); Opticalnetwork virtualization; Physical layer impairments (PLIs);Virtualization infrastructure composition.

I. INTRODUCTION

C loud services [1,2] are emerging as an essential com-ponent of the enterprise IT infrastructure and, conse-

quently, one of the fastest growing business opportunitiesfor Internet service providers and telecom operators [3].Many enterprises are moving their services toward cloudinfrastructures. According to Alcatel-Lucent’s recent fore-casts [4], by 2014 80% of all new software will be availableas cloud services and—still by 2014—there will be 30% ofannual growth in enterprise cloud services.

Cloud services are characterized by their performanceand availability, which are highly dependent on thecloud physical infrastructure. The cloud physical infra-structure comprises the data center (DC) infrastructure

(i.e., computing, storage, and general IT resources) as wellas the network connectivity interconnecting DCs with eachother and the users. In the rapidly growing cloud comput-ing market, scalability and performance optimization ofthe cloud infrastructure are becoming major challenges,with key focuses on both networking and control technol-ogies. In fact, service providers have to cope with 1) cloudservices delivered by more and more geographically dis-tributed DCs, 2) ever-increasing requests from users andDC providers for application-tailored DC infrastructureswith guaranteed quality of service (QoS), 3) very highthroughput and very low latency performance require-ments, 4) resource dynamicity and elasticity (i.e., flexiblestorage and on-demand computing with bandwidth connec-tivity requirements), and 5) seamless resource/servicemigration [5]. In order to address the aforementioned chal-lenges and effectively support emerging cloud services, thefuture cloud infrastructure needs to utilize

• a high-performance and high-capacity optical networkinfrastructure for inter-DC connectivity,

• a tighter integration between the optical network andservices/resources provided by DCs,

• coordinated virtualization of the optical network andDCs to create application-specific and independentvirtual infrastructures (VIs) with guaranteed QoS coex-isting over the shared underlying physical substrate.

A cloud infrastructure supporting the combination of theabove features allows realization of an end-to-end cloudservice provisioning architecture that automatically andefficiently coordinates the DC infrastructure (i.e., comput-ing and storage) with the required optical network connec-tivity services and provides the converged services to usersin the form of DC as a service (DCaaS). DCaaS enables thecoordinated operation of dynamic, large-scale, and globallydistributed DCs, and also the on-demand creation of appli-cation-specific and reconfigurable virtual DC infrastruc-tures. A key technology enabler for the realization ofDCaaS is virtualization. Using virtualization [6], multiplecoexisting but isolated application-specific VIs (virtual DCsplus the virtual network) can be composed over the samephysical infrastructure. A major challenge in VI composi-tion is the VI mapping methods for providing practical (im-plementable) and optimal mapping solutions with efficientresource utilization and maximum revenue. This is becom-ing even more complex in the DCaaS framework, whereDCs are geographically distributed and the DCs and thehttp://dx.doi.org/10.1364/JOCN.5.00A162

Manuscript received May 10, 2013; revised August 16, 2013; acceptedAugust 16, 2013; published September 19, 2013 (Doc. ID 190333).

The authors are with the High Performance Networks Group (HPNG),Department of Electrical and Electronic Engineering, University of Bristol,UK (e-mail: shuping.peng@bristol.ac.uk).

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1943-0620/13/10A162-09$15.00/0 © 2013 Optical Society of America

network have to be virtualized in a coordinated manner.However, in most of the existing virtualization methods[7–9], only IT resource virtualization (i.e., computing andstorage) and bandwidth virtualization are considered. In[7], virtual network assignment schemes with and withoutreconfiguration were developed. In [8], the effect of sub-strate path splitting and migration on virtual network em-bedding were investigated, while in [9] a virtual networkmapping algorithm based on subgraph isomorphism detec-tion was proposed that is able to map nodes and links at thesame stage. However, the optical network virtualization[10,11] and the coordinated virtualization of both IT andthe optical network [12,13] are still at the early stageand have not been extensively studied. Moreover, an opti-cal network has its own specific and rich characteristics,e.g., diverse optical transmission technologies and ana-logue nature (i.e., constraints and impairments [14]).Therefore, in order to provide realistic virtualization solu-tions for the DCaaS architecture, these aforementionedfeatures of the optical network need to be taken into ac-count as well. The continuously developing optical networkvirtualization [15,16] and its corresponding control planetechnologies [17] are making this possible.

In this paper, a DCaaS architecture utilizing opticalnetwork virtualization is proposed. Multiple virtual opticalnetwork (VON) composition algorithms with customizedobjectives (cost-, resource-, and impairment-aware) areproposed, taking into account the unique features of theoptical network layer, i.e., impairments and constraints.In order to fit into cloud computing environments, thecoordinated virtualization of the optical network and DCinfrastructures is proposed.

The rest of this paper is organized as follows. The DCaaSarchitecture is depicted in Section II. In Section III, theoptical network virtualization concept is introduced andVON composition algorithms with customized objectivesover a mixed-line-rate (MLR) WDM network are elabo-rated. In Section IV, the coordinated optical network andIT virtualization algorithms for creating VIs are intro-duced. Simulation studies over diverse network scenariosare discussed in Section V. Finally, Section VI concludes thestudies.

II. DATA CENTER AS A SERVICE ARCHITECTURE

In this paper, we propose a DCaaS architecture address-ing the aforementioned requirements and utilizing coordi-nated virtualization of the optical network and DCinfrastructure, as shown in Fig. 1.

In the DCaaS architecture, multiple geographically dis-tributed DCs interconnected by high-performance opticalnetworks apply the infrastructure as a service model to cre-ate virtual data centers (VDCs) or VIs and offer them asservices to large enterprise users. By taking advantageof the benefits of virtualization technologies, the composedmultiple VDCs can be coexisting and running in parallelover the same physical infrastructure but also isolatedamong each other.

As shown in Fig. 1, the proposed architecture comprisesthree layers: 1) the physical infrastructure layer, 2) the in-frastructure virtualization layer, and 3) the DCaaS layer.

The physical infrastructure layer includes the physicalinfrastructures (optical networks and DCs) from multiplenetwork and DC providers.

The infrastructure virtualization layer provides coordi-nated virtualization (slicing) of the interconnected opticalnetwork and DC infrastructures. Mechanisms and strate-gies are developed and adopted in this layer for the homo-geneous abstraction and virtualization (partitioningand/or aggregation) of optical network and IT (computingand storage) resources across DCs and operator opticalnetworks.

The DCaaS layer composes converged VDC infrastruc-tures by orchestrating the virtual resources (i.e., VONand IT resources) that are optimally created (by partition-ing and/or aggregation) from the infrastructure virtualiza-tion layer based on user/application demands. Each of thecomposed VDCs will also be controlled, managed, andoperated by the management and control functionalitiesprovided in this layer.

In the DCaaS architecture, the virtualization of the ITresources of DCs has reached a mature and commercialstage. However, the virtualization of the optical networkand the coordinated virtualization of both optical networkand IT resources are still under initial study [18]. In thefollowing sections, we are going to focus on these two areas.

III. OPTICAL NETWORK VIRTUALIZATION

In the cloud environment, optical network virtualizationplays a key role in interconnecting geographically distrib-uted virtual IT resources provided by remote DCs with adynamic and high-performance VON infrastructure.

A. Concept of Optical Network Virtualization

By employing optical network virtualization, physicaloptical network resources are abstracted, partitioned,

Fig. 1. Reference model of DCaaS architecture.

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and/or aggregated into virtual resources, and then com-posed into multiple coexisting but isolated VONs runningin parallel over the shared physical infrastructures. Coex-istence and isolation of multiple VONs are the two mostimportant principles of optical network virtualization.However, compared to other network technologies (i.e.,layer 2 and layer 3), the optical network has its own specificanalogue features including optical layer constraints (e.g.,wavelength/spectrum/bitrate continuity) and impairments(i.e., linear and nonlinear). These optical layer featureswill impact the total number of VONs to be successfullycomposed and also affect the isolation between coexistingVONs. Therefore, when virtualizing an optical network in-frastructure, these inherent optical layer characteristicsneed to be taken into account.

Moreover, the optical network itself is undergoing sig-nificant evolution and facing technological upgrades due tothe development of underlying optical transmission tech-niques, e.g., elastic and flexi-grid optical networks [19],and MLR WDM optical networks [20]. The impact of thesenew transmission techniques on optical network virtuali-zation also needs to be investigated.

AnMLRWDMnetwork is considered as an intermediateand realistic optical network upgrade candidate [21].In MLR networks, an optical fiber link can carry wave-length channels with a variety of capacities (e.g.,10∕40∕100 Gbps). Since the reach of high-bitrate channelscould be limited when using basic modulation formats dueto the impact of impairments, advanced modulation for-mats are adopted for those channels [e.g., dual-polarizationquadrature phase-shift keying (DP-QPSK) for 100 Gbps].Due to the availability and flexible combination of multiplevarious bitrates, an MLR WDM network can also providebetter support for the arbitrary and application-specificbandwidth requests from the users of cloud services. Theoptical network virtualization over the MLR network(a single-line-rate WDM network can be seen as a specialcase of an MLR network) is investigated in our work.

B. Optical Network Virtualization Over the MLRWDM Optical Network

Generally, a VON request indicates a virtual topology,stating the attributes of virtual nodes (e.g., the locationof optical nodes, the ports per optical node, the wavelengthchannels per optical link, the supported bitrates, pro-grammability and modulation format of transponders,etc.), the requested bandwidths and latencies of virtuallinks, and how the virtual nodes are interconnected byvirtual links. In the VON composition process, virtualnodes are mapped to physical nodes, while virtual linksare mapped to physical paths by virtual link mappingmethods [16]. In this paper, considering the unique fea-tures of optical networks, three VON mapping methodswith different objectives are proposed to choose availablechannels with proper line rates along physical paths tosatisfy the requirements of VON requests over MLRWDM networks.

1) Cost-Aware VON Composition Method: In MLRnetworks, transponders with higher bitrates can provideattractive volume discount [e.g., the cost of a 40 Gbps tran-sponder can be 2.5 (not 4) times that of a 10 Gbps one [20]].Therefore, for a certain bandwidth requirement of a VONrequest, the choice of line rates (i.e., transponders) willdetermine the cost of the composed VON. The proper selec-tion of transponders can reduce the expense of VON provid-ers if they need to rent virtual resources. In the cost-awareVON composition method, we properly distribute therequested bandwidth among multiple line rates in orderto minimize the total cost of the transponders utilized ina VON.

2) Resource-Aware VON Composition Method: In MLRnetworks, the requested bandwidth can be satisfied by acombination of channels with various line rates. However,this combination affects the network resource utilization.Moreover, considering a pay-as-you-go utility model, allo-cating the exact bandwidth as specified in the request en-sures that users just pay for what they need. For instance,for a 30 Gbps bandwidth request, either one 40 Gbps chan-nel or three 10 Gbps channels can be used. The first optioncan save two channels, while the latter one provides zeroresidual bandwidth. The resource-aware VON compositionmethod chooses the combination of line rates with themini-mum number of channels, saving resources for future VONrequests. In the case of multiple options, the one with theleast residual bandwidth is selected.

3) Impairment-Aware VON Composition Method: InMLR networks, the impact of impairments such as ampli-fied spontaneous emission, chromatic dispersion, andpolarization mode dispersion is similar to that in single-line-rate networks [16]. However, the impact of nonlinearimpairments between different channels is asymmetric,particularly cross-phase modulation (XPM). 10 Gbps chan-nels severely impact 40 or 100 Gbps by a detrimental XPMimpairment. However, the XPM generated from the high-line-rate channels is not so harmful, and neither is theXPM between the channels with the same line rate [21].We assess the impact of impairments using the impairmentmodel elaborated in [22] and select channels with suitableline rates and spectral separation in order to minimizethe impact of nonlinear impairments between MLR chan-nels and guarantee the transmission quality and the isola-tion of coexisting VONs. During the selection of properchannels, the suitable spectral separation is estimatedby calculating the penalty introduced by XPM that is setat less than 0.1 dB.

The impairment-aware method can be used jointly withthe proposed cost-aware and resource-aware methods, re-spectively, as we did in this paper. However, it can also beused together with other transponder selection methods. Aflow chart for using the proposed methods is given in Fig. 2.Three basic phases are included: 1) choosing proper tran-sponders, 2) checking wavelength availability, and 3) VONquality verification and composition. VL �vli ∈ VL� is theset of virtual links in a VON request. For each virtual link,there are K candidate paths to map it. P �Pathk ∈ P� isthe set of physical paths. By using either cost-aware or

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resource-aware methods, the types (bitrate) and number oftransponders will be chosen. After path selection, the avail-able wavelengths will be selected and reserved. For eachactive wavelength, its own quality and impact on other ac-tive wavelengths will be checked by using the analyticalphysical layer impairment (PLI) assessment model de-scribed in [22]. If the quality of all the involved channelsis acceptable (determined by a predefined quality thresh-old), the wavelength will be temporarily assigned to therequested VON. Otherwise, the next available wavelengthor path will be selected, and subsequently the quality veri-fication will be performed. If the quality of all the involvedVONs is assessed to be acceptable, the chosen wavelengthswill be allocated to the VON request. When a VON isreleased, the assigned wavelengths will be released andthe quality of all the involved VONs will be updatedaccordingly.

IV. COORDINATED IT AND OPTICAL NETWORK

INFRASTRUCTURE VIRTUALIZATION

As elaborated in Section II, a VDC/VI is composed ofvirtual IT resources interconnected by VONs. In the pro-posed DCaaS architecture, a VDC is mapped to a physicalinfrastructure (i.e., multiple DCs interconnected by optical

networks) using the infrastructure virtualization and VDCcomposition mechanisms, with the objectives of satisfyingthe VDC requirements (e.g., geo-location of computing/storage resources, computing/storage capacities, networkbandwidth and topology, etc.) and optimizing the physicalresource utilization. Within a VDC, the VON infrastruc-ture can be composed using the methods presented inSection III. However, in order to achieve a holistic optimi-zation integrating IT resources of DCs and the optical net-work infrastructure, we propose the coordinated IT andoptical network infrastructure virtualization method.

In the coordinated virtualization method, a physicalinfrastructure is modeled as a weighted undirected graphcomprising a set of IT and optical network resources(including optical nodes and links). Each resource is asso-ciated with a set of common attributes such as geo-locationand a set of technology dependent attributes such as CPU/storage capacity for IT resources, number of ports andnumber of wavelengths per port for optical nodes, bitrateper channel, and length of optical links.

The proposed method for mapping a VDC to the physicalinfrastructure performs the following steps:

1. The geo-location requirements of virtual nodes in therequested VDC are checked first (i.e., whether the loca-tions of virtual nodes are specified) in order to reducethe potential search space.

2. During the mapping process, virtual IT nodes that re-quire the most resources will always be mapped first. Ifthere is more than one candidate physical node suitablefor a particular requested virtual node, the physicalnode that has more available capacity is selected forthe purpose of load balancing.

3. After mapping each virtual node, the associated virtuallinks that interconnect the newly mapped virtual nodeand the already mapped nodes of the VDC are consid-ered for mapping by using steps 4 and 5.

4. For mapping a virtual link, the existing suitable physi-cal paths that can satisfy the requirements of virtuallinks (i.e., bandwidth and latency) are found by usinga routing algorithm (e.g., k-shortest path).

5. After a suitable physical path is found for a virtual link,which can satisfy all the requirements, taking into ac-count the analogue features (e.g., impairments) in theoptical layer, the requested bandwidth of a virtual linkis mapped over the wavelength channels within aphysical path as stated in the previous section.

The main advantage of this coordinated virtualizationmethod is that the virtual nodes (i.e., virtual IT and net-work nodes) and virtual links in a VDC request can bemapped at a single stage (not two stages, one for the map-ping of all the virtual nodes in the VDC request and theother for the mapping of all the virtual links), as shownin Fig. 3. In the proposed method, a complete mapping stepincludes the mapping of a virtual node (v3 in Fig. 3) andthe virtual links that interconnect the virtual node tothe alreadymapped portion of the VDC (v1 and v2 in Fig. 3).If a mapping step fails due to lack of resources, the

Fig. 2. Flow chart of VON composition methods.

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algorithm can return back to the last successful mappingstep and continue mapping from there.

V. SIMULATION STUDIES

In this section, the performance of the proposed infra-structure virtualization methods is evaluated in differentnetwork scenarios.

A. VON Composition in a MLR WDM Network

The National Science Foundation (NSF) topology with14 nodes and 21 links and the COST239 European OpticalNetwork (EON) topology with 11 nodes and 26 links areadopted, as shown in Fig. 4.

The network is transparent, i.e., there is neither wave-length conversion in the intermediate nodes nor bitrateconversion or grooming capability between different linerates. Three types of bandwidth per virtual link are re-quested: type I [10–40 G] (50%), type II (40–120 G] (40%),and type III (120–360 G] (10%). The k-shortest path routingalgorithm is used to map virtual links to physical paths,where k � 2. The cost of 10∕40∕100 Gbps transpondersis normalized to 1, 2.5, and 5 units [23], respectively.Physical layer parameters used in the impairment assess-ment are given as follows: each fiber span consists of asingle-mode fiber (SMF) and a dispersion-compensatingfiber (DCF). SMF: the dispersion parameter D is17 ps∕�nm � km�, the nonlinear coefficient r is 1.3∕�W � km�, the attenuation parameter α is 0.2 dB∕km, thenonlinear index coefficient n2 is 2.6 � 10−20 m2∕W, andthe effective area Aeff is 80 � 10−12 m2; DCF: D is −92 ps∕�nm � km�, r is 6.0∕�W � km�, α is 0.6 dB∕km, n2 is3 � 10−20 m2∕W, and Aeff is 20 � 10−12 m2. The input powerPin is 1 mW, the amplifier noise figure NF is 4 dB, theoptical filter bandwidth B0 is 40 GHz, and the referencebandwidth that measures optical signal-to-noise ratio(OSNR) Bref and the bandwidth that measures NF Δfare 12.5 GHz. The International Telecommunication UnionTelecommunication Standardization Sector (ITU-T)G.694.1 grid, anchored to 193.1 THz, is adopted. Channelspacing is 100 GHz (0.8 nm). BERth before forward errorcorrection is set to 10−6. The impairment assessment model

in [22] is used to evaluate the impact of the impairmentsduring the process of composing VONs.

Each simulation randomly generates 1000 VON re-quests. VON requests follow a Poisson process with anaverage inter-arrival period of 20 time units, and eachVON has an exponentially distributed holding time (lifetime) with mean value varying from 200 to 1000 timeunits. The failure rate of VON composition is taken asthe performance comparison criteria, defined as the ratioof declined VON requests relative to the total. In orderto make a fair comparison of different VON compositionmethods, the VON request sequence is the same in eachsimulation. Each result, presented as statistical values,is collected by running the simulation four times.

1) Cost-Aware Versus Resource-Aware VON Composi-tion: In Fig. 5, the distribution of a certain requestedbandwidth among multiple line rates (10∕40∕100 Gbps)is shown for each VON request from No. 490 to No. 500using (a) cost-aware and (b) resource-aware methods.

We can see that the line rate distribution is different forsome of the VON requests, e.g., for request No. 496, therequested bandwidth 20 Gbps is distributed among 2 �10 Gbps by using the cost-aware method (the cost is 2 unitsand two channels are occupied) but 1 � 40 Gbps by usingthe resource-aware method (the cost is 2.5 units and onlyone channel is occupied), which successfully reflects theobjectives of the two methods.

The VON composition performance using the two meth-ods is compared under both NSF and COST239 EON

Fig. 3. Coordinated IT and optical network infrastructurevirtualization (only the related nodes and links are shown).

Fig. 4. Network topology: (a) NSF topology with 14 nodes and 21links and (b) COST239 EON topology with 11 nodes and 26 links.The number on each connection indicates the length of each physi-cal link, and the unit is kilometers.

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network topologies. In order to evaluate how each VONcomposition method performs individually, the impact ofimpairments is not taken into account here, that is, thefirst available wavelength channel is assigned. The resultsare shown in Fig. 6. We can see that the resource-awareVON composition method performs better than the cost-aware method under various VON holding times in termsof the failure rate of VON composition. It is because theresource-aware method uses fewer resources comparedto the cost-aware method, which can save more channelsfor the coming VON requests and reduce the failure rateof VON composition.

Moreover, the results collected from the two networktopologies follow the same trends. However, we also findthat both VON composition methods perform better underthe COST239 topology, which reflects the impact of net-work nodal degree on VON composition. The improvementis due to the fact that the COST239 topology is highly con-nected with an average network nodal degree of 4.73,which is higher than that of the NSF topology, i.e., 3. Inorder to understand this better, we can put it in an extremesituation, i.e., a full mesh network topology. In a full meshnetwork, virtual links can be directly mapped into physicallinks one to one, while in a network with lower nodal de-gree a virtual link may need to traverse multiple physicallinks that need to comply with the wavelength continuityconstraint.

2) Impairment-Aware VON Composition: When theimpairments are taken into account during the selectionof proper resources using resource-aware and cost-awareVON composition methods, the suitable spectral separa-tion Δλ is estimated by calculating the penalty introducedby XPM, which is set at less than 0.1 dB and a bit error rate(BER) that is larger than BERth.

In Fig. 7, the results depict the impact of impairments onboth resource-aware and cost-aware VON compositionmethods under the two topologies. Again we find thatthe two VON composition methods with the considerationof impairments perform better under the COST239 net-work topology in terms of the failure rate of VON compo-sition. This is because, besides the difference in thenetwork nodal degree already elaborated in the previoussubsection, the average distance between node pairs onthe shortest path [24] in the COST239 topology (i.e.,835.1 km) is shorter than that in the NSF topology (i.e.,2309.9 km), which means that in the COST239 topologya virtual link will traverse a shorter distance, experiencinga lower effect of impairments.

In Fig. 8, the impact of the total number of wavelengthsper link on the two VON composition methods is evaluatedwhen taking the PLIs into account. As the number ofwavelengths per link increases, more VON requests areaccepted because of the increased available resources.

Fig. 5. Distribution of the requested bandwidth for each VON re-quest fromNo. 490 to No. 500. (a) Cost-aware method, (b) resource-aware method. The VON holding time is 400 time units, VONNo. 490 to No. 500, and the number of wavelengths per link is 40.

Fig. 6. Comparison of resource-aware and cost-ware VON composition methods under NSFand COST239 EON network topologies. Thenumber of wavelengths per link is 40.

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B. Coordinated Virtualization of IT + OpticalNetworks

Performance of the proposed mapping algorithms isevaluated in terms of the ratio of successful VI compositionconsidering the size of the required VI and limitation ongeo-locations. The NSF topology with 14 nodes and 21 links[see Fig. 4(a)] is adopted for the simulation study. Themaximum number of wavelengths per link is 20. Thenumber of IT nodes of the DC infrastructure is randomly

generated and attached to the randomly selected opticalswitches, and each IT node has a capacity of 100 units.

The size of a requested VI, that is, the number of virtualnodes within a VI, can be controlled within a range. Eachpair of virtual nodes is randomly connected with a proba-bility of 0.5. A special check is performed after each requestis generated in order to guarantee that there will be no un-connected nodes in the requested VI. The network degree ofthe generated virtual topology is uniformly distributed be-tween 2 and 3. The number of virtual IT nodes is randomly

Fig. 7. Impact of impairments on resource-aware and cost-aware VON composition methods under NSFand COST239 EON topologies.The number of wavelengths per link is 40.

Fig. 8. Impact of number of wavelengths per link on resource-aware and cost-aware VON composition methods under NSF andCOST239 EON topologies, taking the impairments into account. The VON holding time is 500 time units.

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generated and attached to the randomly selected virtualnetwork nodes, and each virtual IT node requests a capac-ity between 1 and 20 units. The requested bandwidth pervirtual link is uniformly distributed between 10 and100 Gbps. The geo-location requirement of virtual nodesis optional. Each result presented as statistical values iscollected by running the simulation four times. The wholesimulation study is implemented in a simulation platformthat is constructed withMATLAB, CPLEX, and Visual Stu-dio C#, on a computer with an Intel Core i7-2600S CPU at2.80 GHz, 4 Gbytes of RAM, and a 64-bit operating system.

1) Effect of Backtracking: As we have explained in Sec-tion IV, if a single mapping step (as shown in Fig. 3) fails,the algorithm will call a backtracking function and returnback to the last successful mapping step and continue find-ing a mapping solution.

When a mapping solution cannot be easily found due tothe size of a network, the backtracking attempt will makethe mapping process traverse all the search branches. Itwill severely affect the convergence time and performanceof the VI mapping in large-scale networks. Therefore, weneed to introduce a threshold on the backtracking to limitthe maximum number of backtracking attempts. Here, weconducted simulation studies to evaluate the restriction.The threshold w on the backtracking attempt is deter-mined according to the requested VI size (w � 0 meansthat there is no backtracking, w � ∞ means that the back-tracking attempt is unlimited). In Fig. 9(a), we can see thatthe backtracking can improve the VI mapping performancein terms of the acceptance ratio of VI requests, especiallywhen the VI is larger (e.g., 17.75% for the scenario “VI size:5–7”). Meanwhile, the execution time of finding a mapping

solution is also increased because of more backtracking at-tempts, as shown in Fig. 9(b), that is, 1.57 s for the scenario“VI size: 5–7” and only 0.49 s for “VI size: 2–4,” respectively.

2) Restrictions on Virtual Links and Nodes: In order toreduce the physical resources involved in the VI mapping,the k-shortest hop count (k-SHC) routing algorithm isadopted to find suitable paths for the virtual links. In orderto satisfy the requirement on the latency of virtual links,the length of the physical path needs to be shorter thanthe required virtual link length. The results are shownin Fig. 10, from which we can see that when the restrictionson the virtual nodes (e.g., geo-location) and links (e.g., la-tency) become loose, more VI requests can be accepted. Ex-act mapping (i.e., a virtual link is directly mapped into asingle-hop physical link) is not practical for VI mapping. Ifthere is no restriction on the geo-locations of virtual nodes,that is, a virtual node can be optimally and adaptivelymapped to any available physical node, over 90% of theVI requests can be served by using the proposed algorithm.

VI. CONCLUSIONS

In this paper, the role of optical network virtualization inthe cloud computing and DC environments is discussed. Amultilayer reference model of the DCaaS architecture isproposed with key functional components specified. Theconcept and unique features of optical network virtualiza-tion are described, and the necessity of optical networkvirtualization for the DCaaS architecture is addressed aswell. Taking into account the unique characteristics of theoptical network layer, i.e., impairments and constraints,multiple VON composition algorithms with customized ob-jectives (i.e., cost-aware or resource-aware) are proposedand compared. The performance of the proposed algo-rithms is evaluated over diverse network scenarios. Thesame trends are reflected from the results over differentnetwork topologies. In order to better adapt to cloud com-puting environments, coordinated virtualization of bothoptical network and IT resources is proposed and investi-gated. The VI composition algorithm equipped with back-tracking capability is elaborated. Through simulations, theimpact of the required VI size and the restrictions on therequired virtual resources are evaluated.

Fig. 9. Effect of backtracking on (a) the VI request acceptanceratio and (b) the execution time. “Execution time” is for 100 VIrequests; no geo-location requirement.

Fig. 10. Impact of restrictions on virtual nodes and links. VI sizeis 2 to 4; k � 2 in the k-SHC routing algorithm.

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The proposed VI/VON composition algorithms enableVI/VON providers to customize and provision theVIs/VONs as services to the users according to theirrequirements.

ACKNOWLEDGMENTS

The work has been carried out with the support of the EUfunded FP7 project GEYSERS, LIGHTNESS, EU-JapanSTRAUSS, and the UK funded projects EPSRC PATRONand Photonic Hyperhighway.

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