Comparison of Opaque and Translucent WDM Networks with Different ...

Comparison of Opaque and Translucent WDM Networks with Different Regenerator-Placement Strategies under Static and Dynamic Traffic Giuseppe Rizzelli, Guido Maier, Romolo Longo, Achille Pattavina Department of Electronics and Information, Politecnico di Milano, Via Ponzio 34-35, 20121 Milan, Italy Email: {rizzelli,maier,pattavina}@elet.polimi.it

Abstract—In this paper we compare the performance of opaque and translucent Optical Transport Networks (OTNs) taking into account different regenerator placement strategies. The translucent approach achieves noticeable results under static traffic in terms of saving of deployed resources. On the other hand in the dynamic scenario a translucent OTN suffers from fewer resources and may display high blocking probability. We describe our planning procedure that is composed of two main steps: the first one selects a subset of network nodes that can host regeneration devices solving the Regenerator Placement Problem (RPP) by the means of three different heuristic allocation algorithms; the second one computes the amount of resources (number of transponders and DWDM systems) accomplishing the Routing and Wavelength Assignment with Regenerator Problem (RWA-RP) to satisfy a given set of static traffic demands. This dimensioning procedure is applied both to a translucent and to an opaque implementation of the same OTN showing the CAPEX benefit of the first approach. Subsequently both translucent and opaque versions of the OTN are simulated under dynamic traffic, displaying the greater robustness of the opaque case to blocking, no matter what regenerator placement algorithm has been adopted. To back up this conclusion we carried out a further analysis on the power consumption. Index Terms—Regenerator Placement Problem (RPP), sparse placement, Routing and Wavelength Assignment with Regenerator Problem (RWA-RP), translucent Optical Transport Network (OTN).

I. I NTRODUCTION The demands for higher bandwidth at lower cost is increasing substantially in today’s communication networks. The continued growth of broadband services is making the service providers and equipment vendors looking for scalable optical networks. One of the major limitations to scalability is power consumption, which in the case of OTN is very much related to the number of Optical-Electrical-Optical (OE-O) conversions a connection undergoes. O-E-O interfaces are commonly needed to carry out both regeneration (3R: reamplification, reshaping and retiming) and wavelength conversion (while we are still waiting for an all-optical regenerator to appear on the market). Since O-E-O converters are also The work described in this paper was carried out with the support of the BONE-project (”Building the Future Optical Network in Europe”), a Network of Excellence funded by the European Commission through the 7th ICTFramework Programme.

expensive devices, these issues lead to the development of optical-bypass technology: a connection remains in the optical domain from its source to its destination employing nodes such as directionless/colorless Reconfigurable Optical Add Drop Multiplexers (ROADM) or Optical Cross Connects (OXC) [1][2]. As well-known, different kinds of OTNs are currently identified on the basis of their utilization of (O-E-O) devices: opaque, transparent and translucent networks [3][4]. An opaque network is characterized by hosting O-E-O interfaces in the nodes at both ends of each link of the network for every lightpath; this approach simplifies network management, design and control, as it implies a full independence of the logical layer from the physical layer. On the other hand, it requires a huge number of O-E-O devices greatly increasing the total network cost and power consumption. At the opposite extreme we have the transparent OTN: in such a network O-E-O conversions do not occur as all signals bypass the intermediate nodes without regeneration. With optical transparent switching, OTN design and operation become cross-layer problems coupling the physical to the logical layer: transmission impairments and wavelength assignment have to be taken into account jointly with traffic demand when planning the network and assigning resources to the lightpaths. Eliminating O-E-O conversions completely from the network is possible only for limited-size plants because transmission impairments limit the maximum distance reachable from the source node and thus the geographical extension of the network. For most wide-area networks the only viable option is the translucent approach in which both opaque and transparent features co-exist in a node: an O-E-O operation is performed if either the signal quality falls below a certain threshold or λ conversion is needed to avoid wavelength blocking [5][6][7]. Translucent network planning aims at employing the smallest possible number of regeneration resources. Various European Projects (i.e. DICONET, PHOSPHORUS, NOBEL) dealt with translucent dimensioning and in many works [10][11][12] a particular emphasis is given on where to deploy regenerators to minimize the number of rejected connections in a dynamic scenario. Other studies are dedicated to the minimization of the number of transponders and fibers to satisfy a given static traffic [13]. Thus, current literature shows the advantages of

the translucent approach both under static and dynamic traffic. Specifically, in the dynamic scenario, smart 3R placement algorithms seem to make translucent OTN comparable or even better performing than the opaque case. This conclusion is based on the assumption that a pre-assigned total number of transponders is shared by a set of regenerating nodes (i.e. those nodes which can host regenerators) previously selected by some algorithms; in other cases, the maximum number of transponder per regenerating node has been fixed. These assumptions are not very realistic in a green-field dimensioning phase: the total number of regenerators in the network (or in each node) should be a result of planning, rather than being fixed a priori. Moreover, in the cited works no constraints on the number of DWDM systems are considered, sometimes setting their number to infinity in order to avoid blocking due to lack of wavelengths. This tends to compensate the scarsity of 3R resources by giving more possibilities to route a connection in different ways. In this paper we would like to analyze translucent OTNs by a different and, we believe, more realistic approach. First, we minimize the number of 3R resources but we do not constrain it a priori. Second, when comparing the translucent to the opaque implementation of an OTN we also take into account how many extra-DWDM systems are required by “translucency” in the static scenario. Then, we also compare blocking performance in the dynamic scenario. This global point-of-view comparison is presented for the first time to the best of our knowledge. This work is organized as follows: section II presents the OTN model and the network design phase; section III shows the sensitivity of the quantity of extra resources and number of regenerator nodes compared to the opaque case to the 3R placement algorithm; section IV describes the performance of the formerly designed networks when experiencing dynamic traffic; section V describes a power consumption model and evaluation under static and dynamic traffic; the conclusions are drawn in section VI. II. T RANSLUCENT D ESIGN The problem of network design for a translucent OTN can be segmented into two steps as follows: given the topology of the network in terms of switching nodes and links, first we choose a subset of network nodes that are provided with regeneration capability (the so-called S3R set) and then we perform the quantification of resources (transponders and DWDM systems) to satisfy a given set of demands for static optical circuits. Both the steps are carried out by heuristic algorithms that operate under the constraints imposed by the physical layer (propagation impairments). A. Translucent OTN model The OTN model that we consider is composed by the following elements: translucent optical nodes and unidirectional DWDM systems. A translucent node consists of an alloptical non-blocking switching fabric together with a certain number of 3R units (transponders) and is able to switch an

optical signal from an input port to an output port without electrical processing (introducing attenuation). The core of the translucent node is the optical switching fabric. Some ports of it are dedicated to local tributaries via tunable optoelectronic devices. Other ports are used by the transiting signals which can cross the fabric without regeneration or, when needed, can be switched to the pool of transponders to be regenerated before leaving the node (see figure 1). A DWDM line system is composed of a fiber, a set of optical line Erbium Doped Fiber Amplifiers (EDFA), boosters and pre-amplifiers together with a wavelength multiplexer (mux) and a de-multiplexer (demux) at each terminal of the system. We assume that in our OTN model all optical links are equipped with the same type of DWDM systems; a link can host one or more line systems at the same time. In each DWDM system a preassigned maximum number of wavelength channels can be lit on and this number is assumed to be the same for all the systems of the network. We do not consider other optical-domain processing devices such as dispersion compensators, WDM channel equalizers, etc. The physical layer impairment model is based on the computation of Personick’s Q factor [17][18] described in the deliverable D2.1 of Nobel project [19] and run by a network simulator developed in C++. A signal quality threshold Qth has been used to evaluate whether the optical signal needs regeneration or not. Note that Qth =17 dB roughly corresponds to a BER of 10−12 (assuming no FEC performed). B. Choice of the set of regenerating nodes The Regenerator Placement Problem (RPP) is a subproblem of translucent network design: it aims at finding the minimum number of regenerator nodes, their best location and the number of 3R units so that a communication path can be established between every pair of source-destination nodes in the network. RPP has been proved to be NP-complete [15][16]. Let us consider G(N,A) as the physical graph of the network where N represents the set of nodes and A the set of physical links. Being understood that the opaque implementation of the network is when all the nodes have one transponder per transit lightpath, the translucent implementation of the same network can be conceived according to two different approaches: •

Sparse translucent approach: every network node can potentially host 3R units, which means S3R =N . Obviously, this implies that each node can have 3R units, though not all the nodes necessarily have transponders at the end of the next design step;

•

Clustered translucent approach: only a subset of network nodes are identified as regenerating nodes and are given the capability of hosting 3R units. All the other nodes are purely transparent. Thus, S3R ⊂ N .

In this first study we have considered two of several algorithms proposed in literature to accomplish the first subproblem in a clustered translucent network: Nodal Degree First

DWDM system

W input W WDM channeels

Pre - Amplifier

U OLA

X Signal OUT

85 Km spacing

W outp put WDM chaannels

Booster

M

D E M U X

Tributaries

Signal IN

All optical switching fabric

Translucent node

Tunable Transponder (3R unit)

Fig. 1.

Node model and DWDM system for a Translucent network

(NDF) [12] and Central Node First (CNF) [12] algorithms. Before describing the algorithms, let us introduce an useful concept in this kinds of problems: we define the transparency island (TI) [8][9] as the set of nodes that can be reached by a node along the shortest path without using 3Rs. In order to connect two nodes that are not in each others TI, regeneration units are needed at some intermediates nodes. NDF and CNF follow the same procedure: once the algorithm starts, nodes are sorted using a certain criterion; then one node at a time is added to S3R until it becomes a 1-connected and 1-dominating set [10]. This means that: 1) each node which is not in S3R must belong to the transparency island of at least one node in S3R (1-dominating); 2) each node in S3R must belong to the transparency island of at least one node in S3R in such a way that it can reach every regenerating node exploiting S3R -nodes’ transparency islands (1-connectivity). This allows every node in the network to establish at least one lightpath with any other node. A k-dominating and kconnected set would guarantee at least k 3R node-disjoint paths between every source-destination pair (this option is left for future investigation). NDF sorts nodes in decreasing order on the basis of their nodal degree and builds the set S3R sequentially picking nodes from the list. As one node is added to S3R , it is removed from the sorted list and the nodal degree of all its neighbors is decremented by one. Then the list is resorted. S3R continues to grow by adding more nodes to the set until it becomes a 1-connected and 1-dominating set. CNF algorithm ranks every node using the topological “centrality”, i.e. a weight proportional to the number of times a node is crossed by the shortest paths between all the node pairs in the network; the more a node is crossed the

more central it becomes for the network. S3R is constructed adding nodes from the list starting with the one having the highest ranking until the set fulfills the 1-connectivity and 1dominating constraints [10]. C. Quantification of Resources 1 This second step of the translucent design procedure is carried out by solving the Routing and Wavelength Assignment with Regenerator Problem (RWA-RP) [15] for each connection using a greedy algorithm. Regenerating nodes are known as output of the previous step and 3R units are allocated to these nodes only when needed, trying to minimize their total number in the network. After all connections have been set up, unused regenerating nodes, if any, are removed from the original S3R set.

III. S TATIC T RAFFIC D ESIGN R ESULTS As case-study example, design experiments have been performed using the well known PAN European network (28 nodes, 41 edges) [14]. We have assumed a uniform static matrix of demands including one bidirectional request for a 10 Gbit/s connection between each pairs of network nodes. Moreover, the maximum capacity of all DWDM systems is set at 40 lambdas. Results of the dimensioning phase in terms of total number of regenerator nodes, installed 3R units and DWDM systems are reported in Table I for three different values of Qth . They show the clear advantage of the translucent approach (both sparse and clustered) over the opaque one in terms of number of 3R units, as expected. In some cases sparse translucent allows slightly reducing also the number of DWDM systems, thus achieving a clear CAPEX saving. This is due to a better

D ESIGN S TRATEGY

N UMBER OF 3R N ODES

N UMBER 3R UNITS

OF

N UMBER OF U NIDI RECTIONAL

DWDM S YSTEMS Qth = 21dB OPAQUE

28

4720

118

SPARSE

23

716

112

CNF

19

728

114

NDF

10

874

120

formerly designed for the same given static traffic under dynamic traffic (assuming a Poissonian model for lightpath setup and tear-down request generation). Dynamic traffic has been assumed to be uniform on all the node-pairs, as is the static traffic employed in dimensioning the network. Figure 2

PAN Network (28 Nodes, 41 Edges, Qth=21dB) 1

Qth = 19dB 28

4960

118

SPARSE

21

354

114

CNF

16

356

122

NDF

5

368

118

Qth = 17dB OPAQUE

28

4960

118

SPARSE

18

128

118

CNF

2

128

124

NDF

2

132

126

TABLE I N ETWORK - PLANNING RESULTS WITH STATIC TRAFFIC

-2

10

Blocking Probability

OPAQUE

-4

10

-6

10

-8

10

CNF-based NDF-based

-10

10

distribution of load in the network compared to the opaque case, in which routing is purely shortest-path based. Shortestpath routing tends to overload few links, increasing the number of links in which two, instead of one, DWDM systems must be deployed. The clustered translucent approach requires more DWDM systems than the sparse one, due to the fact that transponder locations are constrained. Thus, clustering transponders in a subset of nodes is economically effective only when fullytransparent network nodes have a lower cost than nodes with the capability of hosting 3R units, so to compensate the extra CAPEX for more DWDM systems. This scenario can become realistic when operational costs due to regenerator hosting are high (e.g. larger area occupation in node-housing infrastructures, much larger energy consumptions, need for special equipment for heat dissipation, maintenance costs, etc.). Clustered and sparse translucent implementations tend to converge in terms of number of deployed transponders when Qth decreases. This leads to an enlargement of the transparency island of each node and to the deployment of more DWDM systems due to less λ-converters locations in the newtork. CNF tends to select 3R nodes adjacent one another and located closer to the “centre” of the network than NDF. NDF, on the other hand, reduces the cardinality of the set S3R by placing 3R nodes more scattered over the network topology, sometimes at the edges of it. In terms of number of transponders, NDF is more efficient than CNF. IV. DYNAMIC S CENARIO If the semi-transparent implementation is less expensive in terms of CAPEX compared to the opaque, the drawback of the translucent approach is a worse behaviour in dynamic-traffic conditions. We have compared translucent and opaque network

SPARSE-based OPAQUE

-12

10

0.6

0.7

0.8

0.9

1

Offered Traffic for node pair [Erlang]

Fig. 2.

Blocking probability under dynamic traffic condition

shows the results based on a computer simulation in which the confidence interval is 1% or less at 95% confidence level. It should be noted that blocking can occur for two reasons: lack of free resources and lack of free 3R units. The translucent implementation, no matter if sparse or clustered and regardless of the algorithm used to build the set S3R , exhibits a large blocking-probability gap compared to the opaque implementation. Similar graphs, which we do not report here due to space limitations, have been obtained by carrying out simulations for other values of Qth (i.e. 17 dB, 19 dB). V. P OWER C ONSUMPTION A NALYSIS Nowadays the environmental impact of telecommunications is drawing the attention of the scientific community: energyconsumption minimization is becoming a key target for network design [20]. The translucent approach is intrinsically “green” as it uses less regenerators than the opaque, thus reducing the total network power consumption. We have evaluated power saving with different 3R placement algorithms under static and dynamic traffic scenario. We have assumed the following power-supply values for the equipment represented in figure 1. These data have been obtained after an analysis of device consumption from different vendors: • 3R unit (tunable transponder at 10 Gb/s): 60 Watt; • optical line amplifier: 100 Watt; • mux+demux+booster+pre-amplifier: 280 Watt;

Total Power Consumption in PAN Network (Qth=19dB) under Static Traffic condition 1000 kW

Power Consumption (kW)

707.7

152.3

145.8

151

100 kW OPAQUE SPARSE-based CNF-based NDF-based

Used Algorithms

Fig. 3. Power consumption under static traffic condition (one bi-directional demand for every node pair)

The switching-fabric power consumption has been neglected. Mean Power Consumption in PAN Network under Dynamic Traffic Condition (Qth=19dB)

250 kW

Mean Power Consumption (kW)

160.9 143.8

143.81

149.7

100 kW 73.5

68.7

65.7 59.1

Minimum Traffic Load (0.1 Erlang) Maximum Traffic Load (1 Erlang)

10 kW OPAQUE SPARSE-based CNF-based NDF-based

Used Algoritms

Concerning the dynamic scenario, we assumed that devices could be switched on by a suitable signaling protocol only when needed, i.e. at lightpath set up, and switched off when a lightpath is torn down. For each formerly designed network, we carried out simulations in order to sample the number of devices simultaneously active (proportional to the istantaneous power consumption). Average values for Qth =19 dB, at 0.1 and 1 Erlang are shown in figure 4. At 1 Erlang there is no relevant benefit in power savings if we take into account that the blocking probability displays a similar gap as the one in figure 2. This means that the slightly higher power consumption of the opaque OTN is due to lower blocking compared to the translucent one (i.e. more accepted lightpaths imply more active devices at the same time). At 0.1 Erlang, none of the networks experienced blocked connections during the entire simulation process, which makes them easily comparable; in this case, a dominant role is played by the higher number of DWDM systems that are active per link: as already explained, the shortest-path based opaque network is less efficient than the translucent approach. Among transucent OTNs, the one exploiting CNF outperforms that based on NDF. VI. C ONCLUSIONS In this paper we have investigated the performance of translucent approach compared to the opaque case under static and dynamic traffic. What emerged from this study was the CAPEX-saving behaviour of the translucent approach in the static scenario, particularly with the CNF placement strategy which showed the best trade/off between the required number of 3R nodes, 3R units and DWDM systems. Nevertheless, sparse placement allows further decreasing the quantity of resources even though increasing the number of regenerating nodes. In dynamic traffic conditions, opaque implementation shows better performance due to the inflexible behaviour of the static-traffic-designed translucent networks: as simulation goes on, lack of the few resources causes connections to be routed along different paths than those planned in the dimensioning phase. This makes traffic demands experiencing blocking most of the time. These first results suggest us that: 1) more advanced transponder-clustering algorithms may be needed in selecting the set S3R in order to preserve the advantages of a translucent network over opaque also with dynamic traffic; 2) revenues due to CAPEX-savings should be related to a cost per blocked connection in the dynamic scenario, in order to have a global point of view of the translucent vs opaque debate. These are the purposes of our next studies. R EFERENCES

Fig. 4.

Power consumption under dynamic traffic condition

Results in figure 3 are related to the static traffic situation. We assume that in static conditions devices are permanently active and thus power consumption is proportional to the number of devices that have been installed. In this scenario the advantages of translucent architecture in terms of power saving are quite clear.

[1] J. Berthold, A.M. Saleh, L. Blair, J.M. Simmons. Optical Networking: Past, Present, Future; Journal of lightwave technology, vol.26, no 9, May 2008. [2] P. Roorda, B. Collings. Evolution to Colorless and Directionless ROADM Architectures; Optical FIber communication/National Fiber Optic Engineers Conference, 2008. OFC/NFOEC 2008, 24-28 Feb.2008. [3] Byrav Ramamurthy, Helena Feng, Debasish Datta, Jonathan P. Heritage and Biswanath Mukherjee. Transparent vs. opaque vs. translucent wavelength-routed optical networks; Optical Fiber Communication (OFC ’99) Technical Digest, San Diego, CA, Feb. 1999.

[4] X. Yang, B. Ramamurthy. Sparse Regeneration in a translucent WDM optical network; Proc. of the SPIE Asia-Pacific Optical and Wireless Communications, (Beijing China, Nov. 2001), pp. 61-70. [5] Gangxiang Shen and Rodney S. Tucker, University of Melbourne. Translucent Optical Networks: the Way Forward; IEEE Communications Magazine, February 2007. [6] Byrav Ramamurthy, Srinath Yaragorla, Xi Yang. Translucent Optical WDM Networks for the Next-Generation Backbone Networks; presented at the IEEE GLOBECOM 2001, San Antonio, TX, Nov. 2001. [7] A. Morea, J. Poirrier. A Critical Analysis of the Possible Cost Savings of Translucent Networks; Proc. 5th Internat. Workshop on Design of Reliable Commun. Networks (DRCN ’05) (Naples, It., 2005), vol. 1, pp. 311-317. [8] A. Saleh. Island of Transparency- An Emerging Reality in Multiwavelength Optical Networking; Proceedings IEEE/LEOS Summer Topical Meeting on Broadband Opt. Net. and Tech., July 1998, p. 36. [9] A. Saleh Transparent optical networking in backbone networks; Optical Fiber Communication Conference 2000, Baltimore, MD, USA. [10] Marcio S. Savasini, Paolo Monti, Marco Tacca, Andrea Fumagalli and Helio Waldman. Regenerator Placement with Guaranteed Connectivity in Optical Networks; Lecture Notes in Computer Science, Volume 4534/2007, Optical Network Design and Modeling, pp. 438-447. [11] Marcio S. Savasini, Paolo Monti, Marco Tacca, Andrea Fumagalli and Helio Waldman. Trading Network Management Complexity for Blocking Probability when Placing Optical Regenerators; High Performance Switching and Routing, 2008. HSPR 2008, International Conference, 15-17 May 2008. [12] Xi Yang, Byrav Ramamurthy. Sparse Regeneration in Translucent Wavelength-Routed Optical Networks: Architecture, Network Design and Wavelength Routing; Photonic Network Communication, Volume 10, Number 1, July 2005, pp. 39-53. [13] Matthias Scheffel. Regenerator Allocation Strategies for Optical Transparency Domains Considering Transmission Limitations; Communications, 2005, ICC 2005, 2005 IEEE International Conference, Volume 3, pp. 1771-1776. [14] S. De Maesschalck et al. Pan-European Optical Transport Networks: An Availability-based Comparison; Journal of Photonic Network Communications, vol.5, no 3, May, 2003. [15] Arunabha Sen, Sudheendra Murthy, Subir Bandyopadhyay. On Sparce Placement of Regenerator Nodes in Translucent Optical Networks; Global Telecommunications Conference, 2008. IEEE Globecom 2008. [16] S. Chen and S. Raghavan. The regenerator location problem; working paper Smith School of Business, Uni. of Maryland, 2006. [17] S.D. Personick, ”Receiver Design for Digital Fiber Optic Communication Systems, I”, Bell Syst. Tech. J, Vol. 52, No. 6, July-August, 1973, pp. 843-874. [18] M. Yannuzzi, M. Quagliotti, G. Maier, E. Marin-Tordera, X. MasipBruin, S. Sanchez-Lopez, J. Sole-Pareta, W. Erangoli, and G. Tamiri, Performance of translucent optical networks under dynamic traffic and uncertain physical-layer information, in Proceedings of the 13th IFIP/IEEE Conference on Optical Network Design and Modelling (ONDM 2009), Braunschweig, Germany, February 2009. [19] Deliverable D2.6 of Nobel Project, Phase 1. Network Simulation ready for dynamic transparent optical network; (A7.1 part). [20] Ceuppens, L.Sardella, A.Kharitonov, D.Juniper Networks Inc, Sunnyvale, CA; Power Saving Strategies and Technologies in Network Equipment Opportunities and Challenges, Risk and Rewards; Applications and the Internet, 2008. SAINT 2008. International Symposium on. Pages: 381-384.