Angelou, M

Azodolmolky, S

Colle, Didier

De Groote, Maarten

Klonidis, D

Pickavet, Mario

Staessens, Dimitri

Tomkos, I

Verbrugge, Sofie

English

Ghent University Academic Bibliography

Techno-economic Analysis of a DynamicImpairment-Aware Optical NetworkD. Staessens1, M. Angelou2, M. De Groote1, S. Azodolmolky2, D. Klonidis2,S.Verbrugge1, D. Colle1, M. Pickavet1 and I. Tomkos21Ghent University - IBBT, Gaston Crommenlaan 8 bus 201, 9050 Ghent, Belgium2Athens Information Technology, 19.5 km, Markopoulo Ave., 19002, Peania, Athens, Greecedimitri.staessens@intec.ugent.be, mang@ait.edu.grAbstract: We quantified the performance of impairment-aware networking in terms ofcapital and operational expenditures and compared it to an impairment-unaware solution inthe presence of nodes that bear different degrees of flexibility.© 2010 Optical Society of AmericaOCIS codes: (060.4510) Optical communications; (060.6718) Switching, circuit1. IntroductionWhen planning or upgrading a long haul core network it is important to optimize investments by balancing the costand frequency of equipment installations. Previous work has shown the economic benefits of transparent networking,with Capital Expenditures (CapEx) savings up to 50% by avoiding unnecessary optical-electronic-optical (O/E/O)conversions [1] and significant Operational Expenditures (Opex) savings due to the minimal power consumption ofphotonics [2]. However in a transparent network as the physical-layer impairments accumulate, the quality of transmis-sion (QoT) degrades and therefore error free detection of the optical signal can be achieved only for a finite distance [3].In this context Impairment Aware (IA) transparent networks make maximum use of the optical reach as this leads tolower blocking rates and more efficient utilization of resources.In addition to the photonic switches that effectively enable transparency, reconfigurable optical switching compo-nents are adopted due to their flexibility to support dynamic traffic in a cost-effective manner [4]. Reconfigurableoptical networks allow operators not to over-provision their network with equipment meant to serve future variationsin traffic. In this work we study CapEx and OpEx reductions achieved by an IA networking solution under the flexibil-ity constraints of different node architecture and show that the IA solution achieves better resource optimization whencompared to an Impairment Unaware (IUA) solution without QoT considerations.2. MethodologyTo assess the IA network planning, we dimension a reference network, comprising of 14 nodes and 23 bidirectionallinks, using the offline IA Routing and Wavelength Assignment (IA-RWA) algorithm proposed in [5]. The IA-RWAalgorithm is compared to an IUA method that does not consider any physical-layer constraints. The two algorithmsare fed with the same traffic matrices and their output (essentially the lightpaths that serve the traffic demands) is usedto calculate the capital and operational costs, by adding the required component costs (amplifiers, add/drop terminals,transponders, network interfaces) of each solution. The IUA-RWA algorithm that was employed stems from the IA-RWA algorithm, yet only taking into account the wavelength availability and continuity constraints. The output of theIUA algorithm is checked afterwards for feasibility by the same QoT estimator used by the IA-RWA algorithm. Toquantify the planning solutions of the two RWA approaches we assume that the network is equipped with 80-channelline systems. If the 80-channel system is exhausted in terms of resources, a parallel line system is installed.Additionally, the analysis is performed for four (4) different types of node architectures that differ in terms offlexibility (bearing or not the features: directionless, colorless, contentionless). The cost of the different architecturesdepends on their physical implementation, presented in detail in [6]. The CapEx and OpEx model for the variousnode components and the capabilities of the four node architectures are summarized in Table 1. It is based on [6] andupdated through a survey of the most recent list prices published by component manufacturers and system vendors [7].Table 1: Component CapEx, Network-related Opex and node capabilities [7]Equipment Relative cost Power consumption [W] Mean Time Between FailuresLong reach 10G Transponder 1 30 250,000EDFA, double stage 1.33 25 500,000EDFA, single stage 1 15 500,0001x4 WSS (80 channels) 2.35 30 300,0001x8 WSS 4.7 40 300,0001x20 WSS 7.05 50 300,0001x40 WSS (not commercially available) 10.58 60 150,000Splitter(cost per port) 0.05 - -Combiner(cost per port) 0.13 - -AWG 0.7 - -Casing Node 2.5 - -Casing Network Interface 1-4 2-8 -Casing AWG 0.5 - -Floorspace (Cost per square meter per year) 0.27 -Power (Cost per kW per year) 0.79 - -Repair cost per WSS failure 0.2 - -Repair cost per Transponder failure 0.1 - -Repair cost EDFA double/single stage 0.1 - -Node type Directionless Colorless ContentionlessR-OXC6 no no yesR-OXC4 no yes noR-OXC3 yes yes noR-OXC2 yes yes partly3. ResultsWe consider a traffic load scaling from 0.8 to 1.1, where load 1 corresponds to a traffic matrix with one 10-Gb/sconnection request per pair of nodes (i.e. 182 lightpaths for the 14-node reference network). The CapEx results withrespect to the induced traffic load for R-OXC6 are depicted in Fig. 1 (left). For load 0.8, the two methods yield thesame CapEx as both can serve the traffic with the 80 available channels. As the load increases, the cost of the IAnetwork solution rises slightly due to the number of transponders, related to the increased number of demands, but itcan still serve all of the demands on a single 80 channel line system. The IUA algorithm, however, requires more than80 channels to accommodate the traffic, leading to a significant increase in CapEx. To resolve the physical blockingan extra parallel line system is installed, which translates to a cost increase of around 60% when compared to the IAcounterpart. Fig. 1 clearly shows that the IA solution outperforms the IUA one in CapEx and the respective differenceis directly proportional to the extra equipment that is required to serve the traffic without any blocking. The IUAsolution does not consider the QoT in the lightpath computation and therefore the routing and channel allocation isnot optimized. As a consequence, the IUA approach calls for a capital investment sooner than the IA one as the trafficincreases. The two methods were also assessed in terms of CapEx assuming R-OXC4 (Fig. 1 right). The overall highercomputed cost for R-OXC4 when compared to R-OXC6 is due to the increased flexibility of the node.Fig. 2 illustrates the relative cost for the four architectures for load 0.8 (left) and load 1.1 (right). The cost isdriven primarily by the number of add/drop terminals and the network interfaces. As a consequence, regardless of thetraffic load the most flexible node, i.e. R-OXC2, appears as the most costly. In the OpEx estimations, the repair costsFig. 1: CapEx of IA vs. IUA for R-OXC6 (left); CapEx of IA vs. IUA for R-OXC4 (right).Fig. 2: CapEx of the different node architectures for load 0.8 (left) and load 1.1 (right).Fig. 3: OpEx of IA vs. IUA with R-OXC6 (left); OpEx of IA vs. IUA for load 1.1 (right)and power consumption of the transponders are responsible for a slight increase for both cases when the traffic loadincreases (Fig. 3). Similar to the CapEx results, the sharp increase in the IUA case for loads higher than 0.8 is causedby the extra parallel line systems. Fig. 3 also depicts the computed OpEx for the four different node architectures.Overall the cost to repair the active node equipment accounts for the major part of the OpEx. The OpEx follows thesame trend as the corresponding CapEx when considering the impact of the architecture and R-OXC6 incurs the lowestrequirement in network-related OpEx. However, the cost of a flexible node (e.g. R-OXC2) may be counterbalanced byits superior performance under highly dynamic traffic conditions.4. ConclusionsIn this work we compared the resource optimization achieved by an impairment-aware network planning solution to aplanning solution that is unaware of the signal QoT and showed that the optimization achieved by the former leads tocapital and operational cost benefits. We also studied the role of the node architectures in the cost estimations and theresults indicate that a flexible node induces higher network-related costs.This work was supported by the EC through the FP7 projects STReP DICONET and NoE BONE.References1. M. Gunkel et al., ”A Cost Model for the WDM Layer”, in Proc. Photonics in Switching 2006.2. A. Lord et al., ”OPEX savings of all-optical core networks”, in Proc. ECOC 2009.3. R. Cardillo et al., ”Considering Transmission Impairments in Wavelength Routed Networks”, in Proc. OFC/NFOEC 2006, paper OFG6.4. M. Ruffini et al., ”Cost Study of Dynamically Transparent Networks”, in Proc. OFC/NFOEC 2008, paper OMG2.5. Siamak Azodolmolky et al., ”An Offline Impairment Aware RWA Algorithm with Dedicated Path Protection Consideration”,in Proc.OFC/NFOEC 2009, paper OWI1.6. R. Huelsermann et al., ”Cost modeling and evaluation of capital expenditures in optical multilayer networks”, OSA J. of Optical Network-ing, vol. 7, no. 9, 2008.7. M. De Groote et al., ”Cost comparison of different Translucent Optical Network Architectures”, in Proc. CTTE 2010.

Techno-economic analysis of a dynamic impairment-aware optical network

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Techno-economic analysis of a dynamic impairment-aware optical network

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