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Seediscussions,stats,andauthorprofilesforthispublicationat:https://www.researchgate.net/publication/225299331 AssessmentofthePowerSeriesRouting AlgorithminTranslucent,Transparentand OpaqueOpticalNetworks ArticleinIEEECommunicationsLetters·June2012 ImpactFactor:1.27·DOI:10.1109/LCOMM.2012.032612.120232 CITATION READS 1 29 5authors,including: JoaquimFMartinsFilho HelderAlvesPereira FederalUniversityofPernambuco UniversidadeFederaldeCampinaGrande(U… 145PUBLICATIONS711CITATIONS 39PUBLICATIONS189CITATIONS SEEPROFILE SEEPROFILE DanielAugustoRibeiroChaves CarmeloBastos-Filho UniversidadedePernambuco UniversidadedePernambuco 67PUBLICATIONS299CITATIONS 170PUBLICATIONS752CITATIONS SEEPROFILE Allin-textreferencesunderlinedinbluearelinkedtopublicationsonResearchGate, lettingyouaccessandreadthemimmediately. SEEPROFILE Availablefrom:JoaquimFMartinsFilho Retrievedon:17May2016 IEEE COMMUNICATIONS LETTERS, VOL. 16, NO. 6, JUNE 2012 941 Assessment of the Power Series Routing Algorithm in Translucent, Transparent and Opaque Optical Networks Joaquim F. Martins-Filho, Senior Member, IEEE, Jamersson L. de Santana, Helder A. Pereira, Daniel A. R. Chaves, and Carmelo J. A. Bastos-Filho, Senior Member, IEEE Abstract—In this letter we extend the analysis of the Power Series Routing (PSR) algorithm, which was previously proposed and analyzed for transparent networks only, to the translucent and opaque networks. The PSR algorithm is a bio-inspired impairment-aware routing algorithm that can be optimized and can adapt itself to the network conditions. We demonstrate that the PSR algorithm is also suitable for these network configurations. We compared the PSR algorithm with other well known routing algorithms and we observed that the PSR algorithm achieved the best performance in all cases. Index Terms—Optical networks, routing and wavelength assignment, regeneration, physical impairments. I. I NTRODUCTION I N dynamic wavelength routed optical networks, the routing and wavelength assignment algorithm (RWA) plays a crucial role in the network performance [1]. The impairmentaware RWA algorithm (IA-RWA) has to provide lightpaths with a quality of transmission (QoT) above a predefined threshold, while reserving resources for future demands [2]– [5]. The optical networks can be classified as opaque, transparent or translucent [6]. This classification depends on the usage of regenerators along the network nodes. Electronic regenerators convert the optical signal to the electric domain in order to perform re-amplification, re-shaping and re-timing (3 R regeneration), and then convert it again to the optical domain to transmit the signal to the next link in the lightpath [1]. In opaque networks, all nodes provide regeneration for all by-passing lightpaths. On the other hand, none of the nodes presents the regeneration capability in transparent networks. In translucent networks, some of the nodes have a certain number of regenerators, whereas the other nodes are transparent [1]. Translucent networks can be viewed as a compromising solution that can achieve a high transmission performance, such as in opaque networks, but with a lower cost, such as in the transparent case. We have proposed an IA-RWA algorithm called Power Series Routing (PSR) algorithm that can be optimized and can adapt itself to the network conditions [7] [8]. This is possible due to the bio-inspired component used to build the PSR. For transparent optical networks, the PSR algorithm presented a Manuscript received February 1, 2012. The associate editor coordinating the review of this letter and approving it for publication was V. Vokkarane. J. F. Martins-Filho is with the Department of Electronics and Systems, Federal University of Pernambuco, Recife, PE, Brazil (e-mail: [email protected]). J. L. de Santana, H. A. Pereira, D. A. R. Chaves, and C. J. A. BastosFilho are with the Polytechnic School of Pernambuco, University of Pernambuco, Recife, PE, Brazil (e-mail: {helder.pereira, danielchaves}@poli.br, [email protected]). Digital Object Identifier 10.1109/LCOMM.2012.032612.120232 superior performance in terms of blocking probability when compared to other well known algorithms, such as optical signal-to-noise ratio routing (OSNR-R) [9], shortest path or minimum number of hops. Besides, Chaves et al. [8] showed that the PSR can automatically adapt itself in the case of network failures or traffic patterns variations to improve the network performance. In this letter, we extend the performance assessment of the PSR algorithm to translucent, opaque and transparent optical networks. We compare the PSR algorithm with other well known routing algorithms in different scenarios. The rest of the paper is organized as follows. In section II we briefly present the PSR algorithm. Section III describes the simulation setup used in the simulations. The simulations results are presented and discussed in Section IV. Finally, In section V we give our conclusions. II. P OWER S ERIES ROUTING A LGORITHM The PSR algorithm has two stages: the planning phase and the operational phase. The link cost function used by PSR algorithm is defined in the planning phase. In the operation phase, the PSR is used as an IA-RWA algorithm during the network operation. The first step of the planning phase is to choose the input variables for the cost function. Chaves et al. [8] used the normalized link length and normalized link availability as the input variables for the cost function. The normalized link length di,j between the nodes i and j is defined by: di,j = i,j , max (1) where i,j is the length of the link connecting the nodes i and j and max is the maximum link length in the network. The normalized link availability ai,j is defined by: ai,j = λai,j λTi,j , (2) where λai,j and λTi,j are, respectively, the number of available and the total number of wavelengths in the link between nodes i and j. In the second step of the planning phase, one needs to describe the cost function in terms of a series. For two input variables, the cost function can be described as [8]: f (di,j , ai,j ) = c 2012 IEEE 1089-7798/12$31.00 N N n0 =0 n1 =0 bn0 ,n1 dni,j0 ani,j1 , (3) 942 IEEE COMMUNICATIONS LETTERS, VOL. 16, NO. 6, JUNE 2012 where bn0 ,n1 are the series expansion coefficients and N is the number of terms used to truncate the infinite series expansion as discussed in [8]. In the last step, an optimizer is used to determine the series coefficients in order to minimize the blocking probability. We used the bio-inspired technique called Particle Swarm Optimization (PSO) algorithm to perform this optimization as indicated in [8]. The optimization process can be formalized as: find the values of the vector bn0 ,n1 that optimize the network performance indicator chosen as the optimization target, in our case, the blocking probability obtained from offline simulations of the network, prior to its operation. The optimization of the function parameters in the planning phase takes into account the physical impairments, which gives to the PSR the characteristics of an IA-RWA algorithm. Moreover, the computational complexity of the online operation of the PSR algorithm consists only of gathering the values of the selected variables chosen and the evaluation of a relatively simple function as shown in (3), the cost function. III. S IMULATION S ETUP We used a version of the SIMTON simulator [10] for translucent networks to assess the network performance both in the planning and in the operation phases. The SIMTON simulator uses the physical layer model proposed by Pereira et al. [9] that quantifies the OSNR degradation and the pulse broadening along the optical signal propagation in an all-optical lightpath. The following physical impairments were considered in the simulations: amplified spontaneous emission (ASE), polarization mode dispersion (PMD), residual chromatic dispersion (CD) and homodyne crosstalk. For translucent networks, we considered the sparse regeneration approach to perform the translucent design. We used a well known regenerator placement (RP) algorithm, called node degree first (NDF) [4] to determine which nodes will present regeneration capabilities and define the number of regenerators per node in the case of translucent network. Fig. 1 shows the network topology used in our simulations. Three scenarios are investigated: transparent, translucent and opaque networks. For the transparent scenario, the network nodes are not provided with regenerators. For the translucent scenario, the NDF algorithm is used to perform the regenerator placement. The numbers inside the network nodes in Fig. 1 indicate the amount of regenerators placed by the NDF in each network node. The NDF algorithm was ran with the following input parameters: 8 translucent nodes and 12 regenerators per node. For the opaque scenario we considered that each node has an unlimited number of regenerators. The traffic follows the dynamic lightpath establishment (DLE), where the call arrivals follow a Poisson’s process and the call hold time follows an exponential distribution. The source-destination pairs of each call are chosen randomly (uniform distribution). If both the OSNR of a given candidate lightpath is above 20 dB and maximum pulse broadening is below 10 % of the bit slot, then the call is accepted. Otherwise, the request is rejected. An accepted call results in the establishment of a circuit switched bidirectional connection in two different fibers between the source and destination nodes. The 0 90 0 140 120 100 12 90 0 90 100 12 100 80 0 0 12 100 100 90 0 90 90 0 12 120 150 150 100 12 100 12 12 60 110 12 0 100 100 0 Fig. 1. Network topology. The numbers inside the nodes indicate the number of regenerators of the translucent network used in the simulations and the numbers in the link indicate the link length in km. blocking probability is obtained from the ratio of the number of blocked calls and the number of call requests. We used the following optical parameters with the Pereira’s et al. model [9] to obtain the simulations results: Transmission fiber loss coefficient of 0.2 dB/km, zero dispersion 2 wavelength of 1450 nm, dispersion √ slope of 0.045 ps/km.nm , PMD coefficient of 0.04 ps/ km, dispersion coefficient and slope for DCF fibers, respectively, of −110 ps/km.nm and −1.87 ps/km.nm2 at 1550 nm, transmitters linewidth of 0.013 nm, input optical power of 3 dBm, input OSNR of 40 dB, bit rate of 40 Gbps, wavelength grid starting at 1528.77 nm (the residual dispersion is zero at 1528.77 nm), switch isolation factor of −38 dB, optical filter bandwidth of 100 GHz; amplifier noise figure 5.5 dB; MUX and DEMUX loss of 2 dB. IV. R ESULTS We compare our approach to other well known routing algorithms: Shortest Path (SP), Minimum Number of Hops (MH), Least Resistance Weight (LRW) and OSNR-based routing (OSNR-R) (references for these algorithms can be found either in [8] or [9]). In all cases we used the first fit (FF) as the wavelength assignment algorithm. Fig. 2, 3 and 4 show the blocking probability as a function of the network load for the PSR and the other RWA algorithms using (a) 12, (b) 24 and (c) 36 wavelengths per link for transparent, translucent and opaque scenarios, respectively. For the transparent network with 12 wavelengths per link, the algorithms present similar performance because the network is strongly limited by the lack of resources (wavelengths) and the routing algorithms perform equally poorly. For 24 and 36 wavelengths per link, some routing algorithms can find the least impaired routes, achieving the best performance. Although the PSR presented the best performance, the blocking probability is very similar to the OSNR-R and SP algorithms in these cases. Note that the blocking probabilities are very similar in Fig. 2(b) and in Fig. 2(c). In this case, increasing of the number of wavelengths per link does not reduce the blocking probability because the transparent network is limited by the physical impairments. MARTINS-FILHO et al.: ASSESSMENT OF THE POWER SERIES ROUTING ALGORITHM IN TRANSLUCENT, TRANSPARENT AND OPAQUE OPTICAL . . . (a) (b) 943 (c) Fig. 2. Blocking probability of a transparent network as a function of the network load for the PSR and other RWA algorithms using: (a) 12, (b) 24 and (c) 36 wavelengths per link. (a) (b) (c) Fig. 3. Blocking probability of a translucent network, with the regenerators placed as shown in Fig. 1, as a function of the network load for the PSR and other RWA algorithms using: (a) 12, (b) 24 and (c) 36 wavelengths per link. (a) (b) (c) Fig. 4. Blocking probability of an opaque network as a function of the network load for the PSR and other RWA algorithms using: (a) 12, (b) 24 and (c) 36 wavelengths per link. For the translucent (Fig. 3) and opaque (Fig. 4) scenarios, one can observe that the PSR algorithm far outperformed all the other RWA algorithms, specially when the opacity of the network increases. It probably occurs due to the adaptation capacity of the PSR algorithm already presented in [8] (limited to transparent scenarios in [8]). This adaptation capacity is a direct result of the offline planing phase of the PSR algorithm, when the best series coefficients bn0 ,n1 of (3) are found considering the network topology, including the number of available wavelengths and regenerators. Therefore, the PSR performs better, relative to the others, for translucent and opaque scenarios because it can make better use of the network resources (wavelengths and regenerators). Moreover, note that the PSR algorithm is robust to deal with different scenarios, it performs as good as (or better than) the best algorithm for all investigated scenarios. The other algorithms do not show this characteristic. For instance, the OSNR-R shows a high performance for transparent networks whereas it performs poorly for the translucent and opaque cases. V. C ONCLUSION This paper presented the first analysis of the Power Series Routing algorithm in translucent and opaque optical networks. We compared the PSR algorithm in three different scenarios for translucent, opaque and transparent networks. Our results 944 IEEE COMMUNICATIONS LETTERS, VOL. 16, NO. 6, JUNE 2012 show that the PSR algorithm outperformed all the other RWA algorithms, specially for translucent and opaque networks, in which its blocking probability can be more than one order of magnitude lower than the others. Although the PSR algorithm had been developed initially for transparent networks, it presented even better results for translucent and opaque networks, because it could adapt itself to the network conditions, making better use of the network resources (available wavelengths and regenerators). ACKNOWLEDGMENT The authors thank FACEPE, CNPq, UPE and UFPE for scholarships and grants. R EFERENCES [1] R. Ramaswami and K. N. Sivarajan, Optical Networks: A Practical Perspective, 3rd edition. Morgan Kaufmann, 2009. [2] R. Martinez, C. Pinart, F. Cugini, N. Andriolli, L. Valcarenghi, P. Castoldi, L. Wosinska, J. Comellas, and G. Junyent, “Challenges and requirements for introducing impairment-awareness into the management and control planes of ASON/GMPLS WDM networks,” IEEE Commun. Mag., vol. 44, no. 12, pp. 76–85, Dec. 2006. [3] S. Azodolmolky, M. Klinkowski, E. Marin, D. Careglio, J. Pareta, and I. Tomkos, “A survey on physical layer impairments aware routing and wavelength assignment algorithms in optical networks,” Computer Networks, vol. 53, no. 7, pp. 926–944, May 2009. [4] X. Yang and B. Ramamurthy, “Sparse regeneration in translucent wavelength-routed optical networks: architecture, network design and wavelength routing,” Photonic Network Commun., vol. 10, no. 1, pp. 39–53, July 2005. [5] G. Shen and R. S. Tucker, “Translucent optical networks: the way forward,” IEEE Commun. Mag., vol. 45, no. 2, pp. 48–54, Feb. 2007. [6] B. Ramamurthy, H. Feng, D. Datta, J. Heritage, and B. Mukherjee, “Transparent vs. opaque vs. translucent wavelength-routed optical networks,” in Proc. 1999 Optical Fiber Communication Conference, vol. 1, pp. 59–61. [7] J. F. Martins-Filho, D. A. R. Chaves, C. J. A. Bastos-Filho, and D. O. Aguiar, “Intelligent and fast IRWA algorithm based on power series and particle swarm optimization,” in Proc. 2008 International Conference on Transparent Optical Networks, vol. 3., pp. 158–161. [8] D. A. R. Chaves, D. O. Aguiar, C. J. A. Bastos-Filho, and J. F. Martins-Filho, “A methodology to design the link cost functions for impairment aware routing algorithms in optical networks,” Photonic Network Commun., pp. 1–18, June 2011. [9] H. A. Pereira, D. A. R. Chaves, C. J. A. Bastos-Filho, and J. F. Martins-Filho, “OSNR model to consider physical layer impairments in transparent optical networks,” Photonic Network Commun., vol. 18, pp. 137–149, 2009. [10] D. A. R. Chaves, C. A. Bastos-Filho, H. A. Pereira, and J. F. MartinsFilho, “SIMTON: a simulator for transparent optical networks,” J. Commun. and Inf. Syst., vol. 25, no. 1, pp. 1–10, 2010. Available in http://iecom.dee.ufcg.edu.br/∼ jcis/Abril%202010/index.html