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Heuristic Algorithms for Regenerator Assignment in Dynamic Translucent Elastic Optical Networks Daniel A. R. Chaves*1, Emerson F. da Silva1, Carmelo J. A. Bastos-Filho1, Helder A. Pereira2, and Raul C. Almeida Jr.3 1 Polytechnic School of the University of Pernambuco, Recife, Pernambuco, Brazil 2 Federal University of Campina Grande, Campina Grande, Paraiba, Brazil 3 Federal University of Pernambuco, Recife, Pernambuco, Brazil *e-mail:
[email protected] ABSTRACT In this paper, we propose two heuristics to perform regenerator assignment in translucent elastic optical networks. The first one tries to use the modulation format with the highest spectral efficiency, whereas the other one aims to transmit the optical signal as far as possible before it undergoes into a regeneration process. The former deploys several regenerators but saves bandwidth. On the other hand, the latter uses a lower number of regenerators, but it uses more bandwidth than the previous one. Different transmission bit rates and format modulations are considered in our simulations. The transmitter SSE noise and optical amplifiers ASE noises are considered in the physical impairments evaluation. We perform simulations to analyse the CapEx-performance trade-off of both strategies in terms of number of regenerators, available slots and blocking probability. Keywords: regenerator assignment, translucent optical network, elastic optical network, 3R regeneration. 1. INTRODUCTION Elastic optical networks (EON) have been proposed as an efficient strategy to accomplish an improved use of available bandwidth in optical fibers [1]. In these networks, the WDM channels are subdivided in narrower slices of spectrum, known as slots or frequency slots (typically 6.25 GHz or 12.5 GHz) [1]. This arrangement may accommodate signals with different bandwidths in a more efficient manner, since it is possible to assign just the number of slots that covers the required signal bandwidth. On the other hand, in WDM systems one must assign an entire channel even whether the required signal bandwidth is, for instance, one half or one third of the total channel bandwidth. Another important characteristic of EON regards on the use of different modulation formats for the signals in an efficient manner [2]. The transmitted signals in EON may accumulate physical impairments along its transmission [2]. The bit error rate (BER) of the impaired signals may become excessively high at the receiver end, which might not reach the minimum predefined quality of transmission (QoT) level. The degradation accumulated by the optical signal during its transmission can be mitigated by the use of electronic 3R regenerators [3]. They are used for re-timing re-amplifying and re-shaping the signal. Regenerated optical signals can present high optical signal to noise ratio, even close to the values in the outputs of the transmitters in the source node. The 3R regenerators have properties that are very useful in optical networking, such as: spectrum conversion and modulation format conversion [4]. The spectrum conversion property allows the regenerators to break the spectrum continuity constraint that the optical lightpaths must comply in transparent optical networks (TONs). This constraint for TONs stands for the fact that the same slice of the spectrum must be used from the source to the destination node. The modulation format conversion allows the same lightpath to use different modulation formats in different transparent sections of the lightpath. Typically, for a given transmission bit rate, modulation formats with high spectral efficiency requires narrower bandwidth than modulation formats with low spectral efficiency. Nevertheless, modulation formats with high spectral efficiency are typically degraded more severely with transmission impairments than the modulation formats with low spectral efficiency. In summary, for the same bit rate, the higher is the spectrum efficiency of the modulation format, the narrower is the bandwidth required and shorter is the transmission allowed to comply with the QoT requirements [4,5]. Thus, short transparent sections allow using modulation formats with high spectral efficiency (saving spectrum), whereas long transparent sections require modulation formats with low spectral efficiency (to cope with physical impairments) [4,5]. In summary, the deployment of regenerators in optical networks can improve its performance since the regenerators can simultaneously improve the quality of the optical signals, save spectrum and break the spectrum continuity constraint, which are the reasons for most of the request blocking occurrences. Optical networks that allow both optical bypass and signal regeneration of the lightpaths are named translucent optical networks [3]. Such networks accomplish better CapEx-performance trade off, when compared to both opaque (all lightpaths are regenerated in every node) and all optical networks (all lightpaths are optical bypassed without regeneration in every node) [3]. In translucent networks with dynamic traffic, there are two different decisions that must be taken regarding the regenerators: the regenerator placement (RP) and the regenerator assignment (RA) [3]. The first one decides which nodes of the network should be equipped with 3R regenerators and how many regenerators should be placed in each one of these nodes. It is usually defined at the design 978-1-4673-7880-2/15/$31.00 ©2015 IEEE
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process of the network, i.e. prior to the network operation. The second one takes place at the operation phase and the network control plane must decide, for each call request, whether and at which node, the signal must be regenerated. In this paper we propose two heuristics to perform RA in translucent optical networks. The first one tries to use the modulation format with the highest spectral efficiency. As a consequence, it uses several regenerators but saves spectral bandwidth. The second heuristic tries to establish the longest transparent sections. This approach uses less regenerators but results in a higher bandwidth usage. We perform some simulations using both approaches aiming to analyse the CapEx efficiency in terms of number of regenerators and available slots. 2. HEURISTICS FOR REGENERATOR ASSIGMENT 2.1 Node and modulation formats assumptions We assumed that the 3R regenerators are able to perform both spectrum conversion and modulation format conversion. Each node is equipped with a pool of R shared virtualized regenerators [4]. Lightpaths may freely use these R regenerators until eventually all regenerators are assigned to other lightpaths. Each regenerator has a capacity to regenerate streams of a specific bit rate BR. It is possible to combine several regenerators in order to regenerate streams of higher bit rate. For example, if all regenerators at the node A have BR = 100 Gb/s (as assumed in this paper) a demand for 100 Gb/s requires one regenerator, whereas a demand of 400 Gb/s requires 4 regenerators. The deployed architecture of the node is similar to the one proposed by Jinno et al. [4]. The available modulation formats are indexed in a list from 1 to M, in which a lower index means a highest spectral efficiency of the modulation format.
Figure 1. Topology used or the simulations. 2.2 Proposed heuristics for regenerator assignment In this paper we propose two heuristics to perform regenerator assignment. They are invoked after the call admission controller (CAC) of the control plane executes the routing algorithm for the current incoming request.
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Our RA algorithm decides not only whether and at which nodes the regenerator must be used. It also decides, the modulation format to be used and it invokes the spectrum assignment (SA) algorithm for each transparent section. We also assume that call requests demand different bit rates, which is an input for both proposed heuristics. One must observe that each different pair of modulation-format/bit-rate has different requirements in terms of number of slots (bandwidth) and minimum optical signal to noise ratio (mOSNR). In summary, both heuristics are able to perform RA, spectrum and modulation format conversions, as well as find both transparent and translucent lightpaths. The proposed heuristics are named as First Longest Reach Regenerator Assignment (FLR-RA) and First Narrowest Spectrum Regenerator Assignment (FNS-RA). They are presented in algorithms 1 and 2, respectively. There are three functions used in the algorithms: IsThereFreeRegenAt(nx,br), a Boolean function that returns true if there are unused regenerators at the node nx to regenerate streams of br bit rate, and false otherwise; isThereSpectrumAndOSNR(ns,nx), a boolean function that returns true if there is at least one modulation format capable of being assigned to the transparent segment between nodes ns and nx in terms of both OSNR and available spectrum, and false otherwise; and isThereSpectrumAndOSNR(ns,nx,m) which do the same of the previous one, but for a specific modulation format m. FLR-RA is presented in Algorithm 1. FLR-RA tries to perform the regeneration (if it is needed) as far as possible from the source node. Given a route P with N nodes, P = {n0,n1,..,nx,..,nN-1}, and considering the list of allowed modulations formats, it establishes the longest transparent segment that meets both OSNR and spectrum availability criteria from the source node n0 to a node in P with regeneration capability, say nx. If such a node exists, the signal is regenerated at nx, and it becomes the new source node and the process restarts until the destination is reached. FNS-RA is presented in Algorithm 2. FNS-RA tries to save spectrum in the network by always trying to use the highest allowed spectral efficiency modulation format in the transparent segment. Given the route P, the algorithm starts by considering the modulation format with index m=1. Then, it searches for the longest transparent segment that meets both OSNR and spectrum availability from source node n0 to a given node, say nx, with regeneration capability. If there is no solution for m=1, the algorithm tries to find, sequentially, the shortest transparent segment from n0 to nx for m=2, 3, …. If a transparent segment is found, the signal is regenerated at nx such node becomes the new source node and the process starts again until the destination node is reached. 3. SIMULATION SETUP AND PHYSICAL LAYER ASSUMPTIONS 3.1 Simulation setup For each network simulation we generate a set of call requests. The call arrivals follow a Poisson process and the call holding time follows an exponential distribution, thus characterizing a dynamic lightpath establishment (DLE) scenario. The simulations are performed with a network load of 100 Erlangs (excepted when indicated otherwise). The source-destination node pair for each call is randomly chosen by using a uniform distribution. Each call request is established through a unidirectional circuit switched lightpath. The routing and spectrum assignment algorithm searches a candidate lightpath for the incoming call request. We used the shortest distance path as the routing algorithm and the first fit policy for spectrum assignment. The transmission bit rate of each call is randomly chosen among 100, 200 or 400 Gb/s. Since this paper aims to study the impact of the regenerator assignment in elastic optical networks we regenerator the same amount of regenerators in all nodes of the network. 3.2 Physical layer consideration The network topology used in the simulations is shown in Fig. 1. There is a pair of optical fibers linking the nodes in the network. Each link is composed by several optical spans with a distance between optical EDFA amplifiers of approximately 80 km. The EDFA gain compensates for the losses. Both EDFA ASE and transmitter SSE noise impair the transmitted signals along the network. The noise figure of the EDFAs is 5 dB. The transmitter has an optical signal to noise ratio of 40 dB. The signal power at transmitter is 0 dBm. Three modulation formats were considered: 4 QAM, 8 QAM and 16 QAM. We assumed a maximum bit error of 10-3 (with FEC) to evaluate the minimum required OSNR (mOSNR). The mOSNR is evaluated as proposed by Essiambre et al. [6]. Each network link is considered with 120 (excepted when indicated otherwise) available slots of 12.5 GHz. 4. RESULTS By definition of the algorithms, the solutions provided by FLR-RA use more bandwidth than the solutions found by FNS-RA. However, FNS-RA usually uses more regenerators than FLR-RA. This is because FLR-RA tries to use small number of regenerators, whereas FNS-RA tries to use small portion of spectrum. Figure 2a depicts the network blocking probability as a function of the number of regenerators per node. One can observe that both
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algorithms show a floor for the blocking probability starting with 18 regenerators per node for FLR-RA and 35 regenerators per node for FNS-RA. There is no gain in blocking probability by deploying more regenerators beyond these points. There is a crossing point for the curves in 18 regenerators per node. For small number of regenerators, FLR-RA archives lower blocking probabilities than FNS-RA. That occurs because FLR-RA usually uses less regenerators in its solutions and there is a limited and low number of regenerators. However, for a larger number of regenerators, beyond the crossing point in the graph, solutions provided by FNS-RA achieve lower blocking probabilities. In this last range, the property of saving spectrum provided by FNS-RA allows more load to be accepted in the network since, on average, the lightpaths are thinner in spectrum. Figure 2b shows the blocking probability as a function of the number of slots per link for a fixed number of 18 regenerators per node (adopted from the crossing point of Fig. 2a). For a small number of slots per link, it is more efficient to save spectrum and FNS-RA performs slightly better than FLR-RA. However, for a large number of slots per link, using more regenerators for saving spectrum is no longer necessary and then FLR-RA outperforms FNS-RA as shown in the Fig. 2b. Figure 2c shows the blocking probability as a function of the network load for a fixed number of 18 regenerators per node. Again, there is a crossing point for the two algorithms on network load around 50 Erlangs. For low values of network load, it is not efficient to use regenerators for saving spectrum and FLRRA performs better than FNS-RA. However, as the load increases, more spectrum is occupied on average and then FNS-RA slightly outperforms FLR-RA because of its saving spectrum property.
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Figure 2. Simulations results for path request blocking probabilities as a function of (a) number of regenerators per node (b) number of slots per link and (c) network load for the proposed algorithms FLR-RA and FNS-RA. 5. CONCLUSIONS In this paper we propose two heuristics to solve regenerator assignment in translucent optical networks: FLRRA, which tries to save the use of regenerators and FNS-RA, which tries to use regenerators for saving available spectrum. We perform simulations for one network topology considering physical impairments in a dynamic circuit switched network using 120 slots per link. In the simulated scenario, if one deploys 17 regenerators or less per node, it is more efficient (in terms of blocking probability) to assign regenerators as aiming to establish longer transparent sections in the lightpath. On the other hand, it is more efficient to assign regenerators to save bandwidth for more than 17 deployed regenerators per node. We conclude from our results that the use of regenerators to reduce the average bandwidth used by the lightpaths is not always the most efficient strategy. Its efficiency depends on the number of regenerators, the number of available slots and the network load. ACKNOWLEDGEMENTS The authors thank to FACEPE, CNPq, UPE, UFCG and UFPE for grants or the educational support. REFERENCES [1] Z. Guoying et al.: A survey on OFDM-based elastic core optical networking, IEEE Communications Surveys & Tutorials, vol. 15, no. 1, pp. 65-87. [2] C. Xiaomin et al.: Multipath routing in elastic optical networks with distance-adaptive modulation formats, in Proc. ICC 2013. [3] D.A.R. Chaves et al.: Novel strategies for sparse regenerator placement in translucent optical networks Photonic Network Communications, vol. 24, no. 3, pp. 237-25, 2011. [4] M. Jinno et al.: Virtualization in optical networks from network level to hardware level, J. Opt. Commun. Netw., vol. 5. pp. A46-A56, Oct. 2013. [5] M. Klinkowski: On the effect of regenerator placement on spectrum usage in translucent elastic optical networks, in Proc. ICTON 2012. [6] Essiambre et al.: Capacity limits of optical fiber networks, Journal of Lightwave Technology, vol. 28, no. 4, pp. 662-701, Feb. 15, 2010.
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