A Heuristic Algorithm for Designing OTN Over Flexi ...

Journal of Communications Vol. 12, No. 9, September 2017

A Heuristic Algorithm for Designing OTN Over Flexible-Grid DWDM Networks João R. Santos1,3, António Eira1,2, and João Pires2,3 1

Coriant Portugal, Lisboa, Portugal. Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal. 3 Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal. Email: [email protected], [email protected], [email protected]

2

Abstract—The efficient utilization of network resources is a fundamental property that is best exploited through multi-layer networks. The Optical Transport Network (OTN) over Dense Wavelength Division Multiplexing (DWDM) is based on the multi-layer concept. These networks, while combining the grooming capabilities offered by the OTN switches with the optical bypass inherent to the Reconfigurable Optical Add-Drop Multiplexers (ROADMs) have the potential to reduce the required network resources and achieve cost efficient designs. Furthermore, an additional degree of flexibility can be added by using flexible-grid solutions for the DWDM layer. In order to explore the full potential of these networks it is import to apply adequate algorithms to design them in an efficient way. With this aim, we propose in this paper a novel heuristic algorithm to solve the routing and grooming problem in the context of OTN/ flexible-grid DWDM networks. The algorithm is based on the concept of an auxiliary graph and operates iteratively, first obtaining an approximated solution and then successively improving it. This algorithm is applied to multiple network scenarios regarding topology, traffic distribution and available transmission formats, in order to quantify the efficiency benefits of deploying flexible-grid formats.

operates in the optical domain, and it is responsible for generating, multiplexing, switching and managing optical channels, each one operating at its own wavelength.

Fig. 1. Multi-layer network [2].

The OTN has a layered structure consisting of several sublayers [1]. The ODU sublayer currently supports five bit rate clients signals, which are approximately equal to 1.25, 2.5, 10, 40, and 100 Gb/s, and are referred to as ODUk, with k=0, 1, 2, 3, 4, respectively. The optical layer has the advantage of being able to provide large optical channels with capacities that can range from 100 Gb/s, available today in commercial applications, up to 400 Gb/s and even 1 Tb/s, expected to emerge in a near future. At the same time, it relies on Reconfigurable Optical Add-Drop Multiplexers (ROADMs) in order to optically bypass the express traffic in transit nodes and provide add/drop functions. On the other hand, as the majority of the client services have a granularity that is typically a fraction of the capacity of these channels, one can use the OTN layer to perform some service grooming as a way to improve the fill ratio of the optical channels. This operation, in connection with optical bypass, has the potential to reduce the amount of required lightpaths, and thus the cost per transported bit, in order to achieve more effective network designs. The central frequencies (and corresponding wavelengths) of the optical channels are standardized by ITU-T using either a constant channel spacing (fixed-grid), or a variable channel spacing (flexible-grid). In a fixed-grid scenario, each channel is typically assigned a 50 GHz width, whereas in a flexible-grid each channel can be assigned multiple contiguous slots of 12.5 GHz according to the spectral requirements of the optical signals [3]. The 100 Gb/s DWDM systems that have been commercialized in the recent years are based on the 50 GHz fixed-grid. However, with the advent of systems

Index Terms—Optical networks, OTN, flexible-grid DWDM, routing, grooming, heuristic algorithm

I. INTRODUCTION In order to support the continuous growth in traffic demand, network service providers must be able to increase the capacity of their transport infrastructure in a scalable and cost effective way. A multilayer structure based on an Optical Transport Network (OTN) over a Dense Wavelength Division Multiplexing (DWDM) configuration, exemplified in Fig. 1, is a possible approach to handle these challenges [1], [2]. The OTN layer operates in the electrical domain and it is responsible for mapping client signals (e.g. Ethernet, SAN, SONET/SDH, etc.) into entities called Optical Data Units (ODUs), as well as for grooming, switching and managing these entities, whereas the DWDM layer

Manuscript received July 11, 2017; revised September 20, 2017. This work was partially supported by the project UID/EAA/50008/2013 funded by Fundação da Ciência e Tecnologia de Portugal and Instituto de Telecomunicações. Corresponding author email: [email protected] doi:10.12720/jcm.12.9.500-509

©2017 Journal of Communications

500

Journal of Communications Vol. 12, No. 9, September 2017

operating at line rates above 100 Gb/s, like for example 400 Gb/s, the use of flexible-grid solutions is expected to become commonplace. To transmit these higher line rates using the traditional fixed-grid would imply the use of highly spectrally efficient modulation formats, inexorably leading to lower transmission reaches, as these formats induce higher signal-to-noise penalties and have worse receiver sensitivities [4]. The flexible-grid concept can potentially bring more efficiency and flexibility to the network [5], but it also brings new challenges to network planning, particularly concerning the equipment selection process. Another possible solution for supporting line rates above 100Gb/s consists in using super-channels. Super-channels are obtained by closely grouping multiple optical sub-carriers between the same source and destination in the optical domain, resulting in a single end-to-end logical entity from the network control plane’s point of view [5], [6]. Super-channels can be implemented by using an appropriate multicarrier technology such as ortoghonal frequency-division multiplexing or Nyquist WDM [5]. Routing and grooming [7], [8] are two of the most important problems in the context of designing OTN/DWDM networks. Routing is responsible for finding the best path for each traffic demand according to a given metric, while grooming is responsible for aggregating various low speed signals into a single higher speed one, in order to increase the optical channels occupancy. In this work, it is assumed that grooming is performed at the ODU layer i.e. ODUk signals of lower order are multiplexed into a higher order ODUk. Grooming can be end-to-end, when it is only performed on the source node of a given path or it can be intermediate, when it can be done in any node along the path. The end-to-end solution is based on devices called muxponders, which feature a static aggregation structure from the client ports to the line-side, while the intermediate solution requires the use of OTN switches. In this paper, we considered both end-to-end and intermediate grooming at the OTN level, while routing takes place at the DWDM layer, which is assumed to be based on a flexible-grid configuration. Although routing in DWDM applications is usually coupled with the spectrum assignment problem [7], [9], throughout this work we will focus solely on the routing and grooming problems, as these are the most relevant in the context of comparing the different solutions, rather than providing a complete solution including the assignment of spectral slots to demands. The capacity limitations introduced by limited spectrum are modeled resorting to channel/slot count constraints. The design of OTN over fixed-grid DWDM networks has recently received some attention in the published literature. In [10] a multi-layer planning model for IP/MPLS over OTN over DWDM networks is proposed. The model is based on integer linear programing (ILP) formulations and due to the difficulty in applying it to large networks a heuristic algorithm is also developed. The impact of traffic grooming is addressed in [11] and ©2017 Journal of Communications

[12], where the authors analyze the improvements granted by using intermediate grooming, implemented through OTN switching on top of a DWDM layer, in comparison with end-to-end grooming, and report an increase in the bandwidth occupation by a factor of 2 and a reduction of network cost higher than 40%. The OTN over flexible-grid DWDM network is analyzed in [13] using an ILP formulation, but assuming only end-to-end grooming. It is shown that in the most favorable scenario the flexible-grid solution leads to a cost reduction of about 16% comparing with fixed-grid solutions. For the sake of comparison it can be referred that in [14], where no grooming is accounted for, that cost reduction is about 10%. Finally, in [15] some studies are performed on the benefits brought by the flexible-grid solution. One of these studies analyzes the cost gains obtained with the increase in the flexible-grid granularity. It is concluded that reducing the network granularity from 50 GHz to 12.5 GHz can lead to cost savings of approximately 40% in the best case. In this paper, we present a novel (integrated) heuristic algorithm to jointly solve the grooming and routing problem in the context of OTN over flexible DWDM networks. It includes a new approach to this problem, using the concept of an auxiliary graph [16], while considering intermediate grooming and a set of transmission formats associated with the flexible-grid. The novelty of our proposed algorithm is that it is more general than the previous works in the sense that it can be applied to either fixed or flexible scenarios and includes any traffic grooming type. Furthermore, it can also deal with super-channel formats, a feature that is not included in the aforementioned works. This paper is structured as follows. Section II describes the node architecture considered in this paper and defines the network design problem to be solved. Section III details the proposed heuristic algorithm and Section IV presents simulation results for different input variables. Finally, Section V gives some concluding remarks. II. NODE ARCHITECTURE AND PROBLEM DEFINITION In this paper, we assume a multi-layer node with the architecture depicted in Fig. 2. It comprises a set of line-cards supported by an ODU switch that are connected to the DWDM infrastructure through a ROADM. The ROADM is responsible not only for adding and dropping optical channels, but also for performing optical bypassing through the switching of optical channels from the incoming to the outgoing optical fibers. The optical bypassing can be complemented with traffic grooming as a way to reduce the cost per transported bit in optical transport networks. By aggregating low bandwidth services into higher data rate pipes, it is possible to improve the bandwidth occupation of optical channels and consequently also improve network efficiency. The ODU switch can be used to groom these services, as well as to interconnect the client and line cards.

501

Journal of Communications Vol. 12, No. 9, September 2017

Fig. 2. Node architecture (adapted from [17]).

The client cards receive the client signals from the upper layers and map them into appropriate ODU containers, with capacities ranging from ODU0 up to ODU4. For example, a 1GbE signal is mapped into an ODU0, while a 100 GbE signal is mapped into an ODU4. The line cards are responsible for processing the Optical Transport Units (OTUs) and for generating optical channels at appropriate wavelengths. The optical transceivers are the source/sink of optical channels and can be based on a single carrier modulation or on super-channels. In this work, we consider the types of optical transceivers described in Table I. The first two types use a single optical carrier and an optical bandwidth of (4×12.5 GHz slots) with the first one operating at 100 Gb/s and using polarization multiplexing-quadrature phase-shift keying (PM)-QPSK and the second one operating at 200 Gb/s and using PM-16 quadrature amplitude modulation (PM-16QAM). The third and fourth transceivers produce a dual-carrier optical signal that is mapped onto a super-channel (logical entity with a unique filtering window) using PM-QPSK and PM-16QAM respectively. Finally, the transceivers of the different line cards are connected to the ROADM add-drop ports, where they are forwarded to the appropriate direction. The optical transceivers operate in a flexible-grid with 50 GHz granularity (e.g., to deploy the dual-carrier solutions in super-channel configurations). Within this context, and attending to the parameters in Table I, the set of transmission formats, F, available to carry the traffic demands along the network can be represented by the transmission tuple [rf, nsf, df, cf], where rf is the total bit ©2017 Journal of Communications

rate (Gb/s), nsf is the number of spectrum slots of the flexible-grid used, df is the optical reach and cf the relative cost. Here, the optical reach (df) refers to the maximum distance an optical signal can travel before its quality degrades to values unintelligible at the receptor side. The modulation technique, denoted as mf, is not included in the tuple because it is determined by the bit rate and the number of slots used. TABLE I: OPTICAL TRANSCEIVER CHARACTERISTICS [12] Format Number

rf [Gb/s]

nsf

mf

df [km]

1

100

2

200

4

QPSK

2600

1

4

16-QAM

600

1.2

3

200

4

400

8

QPSK

3000

2

8

16-QAM

800

2.4

cf

The physical topology of the optical network is represented by an undirected graph G(N, L), where N is the set of nodes and L is the set of links connecting the nodes. The nodes correspond to the multi-layer nodes depicted in Fig. 2. A link corresponds to an optical fiber. As the links are unidirectional we assume that there is one optical fiber for each direction. The maximum number of spectrum slots of 12.5 GHz carried by each fiber is equal to 384, which corresponds to a bandwidth of 4.8 THz. We are also given a traffic-demand matrix T=[tsd], where tsd denotes a traffic demand between the source, s, and destination, d, nodes. The traffic demands correspond to connection requests from the client layers and are expressed in terms of ODUk signals. 502

Journal of Communications Vol. 12, No. 9, September 2017

To route a traffic demand tsd over the optical network G(N, L) one or more lightpaths lp need to be established. A lightpath is defined as a transparent optical connection between two nodes and it is associated with a given format f, chosen from the ones described in Table I. Multiple traffic demands tsd can be groomed onto a single lightpath lp. Our aim is to establish the list of lightpaths lp necessary to route all the traffic demands tsd, while at the same time finding a solution that minimizes the total network cost or the spectrum usage. The heuristic algorithm proposed in the following section addresses this optimization problem.

11: 12:

13:

14: 15: 16:

III. OPTIMIZATION WORKFLOW

17: 18: 19: 20: 21:

The heuristic algorithm proposed here solves the grooming and routing problem in the context of OTN over flexible-grid DWDM networks. The proposed heuristic is based on an auxiliary graph. This auxiliary graph is constructed with the objective of comparing the different options for grooming and routing traffic demands over the physical topology, which is augmented with the already established lightpaths.

22:

A. Construction of Auxiliary Graph The algorithm to construct the auxiliary graph receives as input a path Psd-K from source node s to destination node d. Psd-K belongs to the set of paths calculated for the traffic demand tsd between s and d using a K-shortest path algorithm, which is more carefully detailed in Algorithm 2. The referred path Psd-K is characterized by the sequence of nodes it traverses (Vsd), as well as the links crossed along its path (Esd). Vsd and Esd are subsets of the optical network graph G(N,L). The auxiliary graph is then represented as Gaux (Vsd, Eaux). The nodes Vsd are restricted to the ones contained in path Psd-K and Eaux is the set of edges of the auxiliary graph. Each edge eaux in Eaux has an associated weight w. The set of edges Eaux is constructed as follows. _ Algorithm 1 Auxiliary Graph Construction _ INPUT: Path Psd-K (Vsd, Esd), traffic demand tsd, set of transmission formats F. OUTPUT: Auxiliary Graph Gaux (Vsd, Eaux). 1: FOR every pair of nodes in Vsd, v1, v2, 2: FOR every format f in F, 3: IF the fiber distance between v1 and v2 is larger than the optical reach df of f THEN, 4: Go to 2. 5: END IF. 6: IF the bit rate of demand tsd is larger than the maximum bit rate rf of format f THEN, 7: Go to 2. 8: END IF. 9: IF the links in the path between nodes v1 and v2 do not have enough spectral slots nsf available to support the format f being considered THEN, 10: Go to 2.

©2017 Journal of Communications

503

23: 24: 25: 26:

27: 28: 29:

END IF. Create an edge ev1,v2 between the pair of nodes v1, v2 with weight w equal to the cost cf of a transponder module for the format f being considered. IF the auxiliary graph has no edge between the nodes v1, v2 or if it has an edge with weight w higher or equal to the weight w of the new edge ev1, v2 THEN, Place the new edge ev1, v2 in the auxiliary graph between the nodes v1, v2 of the auxiliary graph. END IF. IF there is already a lightpath lpv1, v2 established between nodes v1, v2 THEN, Go to 21. ELSE, Go to 1. END IF. IF lightpath lpv1, v2 has enough capacity available to carry demand tsd THEN, Create an edge ev1, v2 with weight w=0 (no cost). Do steps 13 to 15. Go to 1. ELSE, Check if it is possible to upgrade the lightpath lpv1, v2 to other format f’ from F. For that, do steps 2 to 15 with some differences. In 6, the bit rate from the demand tsd is added to the bit rate already being carried by lpv1, v2, and then the sum is compared with the format f’ maximum bit rate rf’. In 12, the weight w of the edge ev1, v2 is equal to the difference between the cost cf of the format f of the lightpath and the cost cf’ of the new format f’. END IF. END FOR. END FOR.

The auxiliary graph is the central piece of the algorithm presented in this paper, where the various lightpath possibilities are compared. This concept is an adaptation of the algorithm presented in [17]. Each edge in the auxiliary graph represents an operation performed on a new or existing lightpath, and a combination of edges between the auxiliary graph’s end nodes is a possible solution for the grooming and routing operations. Different actions can be associated to each edge, depending on the steps necessary for its creation. The edges created from Step 1 to Step 15 in Algorithm 1 correspond to setting up a new lightpath, while the ones from Step 16 to Step 24 correspond to using an existing lightpath. Finally, the edges created in Step 26 correspond to upgrading a lightpath. In order to use or upgrade an existing lightpath, it needs to have been set up previously for other demand. The traffic grooming operation corresponds to the case in which more than one traffic demand is accommodated into a single lightpath.

Journal of Communications Vol. 12, No. 9, September 2017

In order to illustrate how the auxiliary graph is constructed let us consider an example based on the network path depicted in Fig. 3a). This path has three nodes and two bidirectional links, each one with the given physical distances. It accommodates two lightpaths, both using the first format (see Table I). The first lightpath carries a demand with 40 Gb/s and the second one a 60 Gb/s demand. Now suppose that a new traffic demand with a rate of 50 Gb/s is requested between node 1 and 3. In the initialization stage, the auxiliary graph includes only three nodes without any edges between them. In order to add edges and the corresponding weights one must follow the steps described.

Firstly, the pair of nodes (1, 2) is analyzed. Starting with the first format in Table I, steps 3 to 15 in Algorithm 1 are executed to perform a set of validations: whether the distance between the nodes is below the reach of the format used, whether the bit-rate of the demand fits into the capacity of the transmission format, and finally whether there are sufficient spectral slots available in the desired path. If the node-pair/format is validated in all aspects, an edge is created with a weight equal to the cost of the format under consideration. If no such edge is yet in the auxiliary graph, it is added to it (see Fig. 3b, left graph).

a) Demand path

b) Auxiliary graphs before and after considering steps 16 to 26. Fig. 3. Auxiliary graph example

Steps 16 to 23 are responsible for evaluating the possibility of using the established lightpaths to carry the new demand. As the existing lightpath between 1 and 2 has a capacity of 100 Gb/s and only uses a fraction of it (40 Gb/s), then the remaining capacity (60Gb/s) can be used to transport the 50 Gb/s demand. As a consequence, a new edge is created with weight 0 (since there is no cost associated with using established lightpaths) and the comparisons from Step 13 to 15 are repeated. Since the edge created previously is already in the auxiliary graph, the weight of both edges must be compared. The new edge has a lower value (0 < 1), so it is placed in the auxiliary graph in place of the previous one (see Fig. 3b, in the right auxiliary graph the edge between 1 and 2 now has weight 0). The process is repeated again but now having as target the pair of nodes (2, 3). For steps 3 to 15, the result is an edge with weight equal to 1 added to the auxiliary graph (see Fig. 3b, left graph). For steps 16 to 23, in this case the lightpath established between 2 and 3 does not have enough capacity available to carry the new demand (only 40 Gb/s available for a 50 Gb/s demand), so in step 26 an upgrade is attempted and the second format is considered. For this format, the condition of Step 3 is verified (1600 km > 600 km), and so the algorithm must go to the third format. In the case of this format, the cost is equal to 2, so the algorithm will create a new edge with weight 1 resulting from the cost difference between the current format and the one used for the existing lightpath (2 – 1 = 1). In Step 13, the weight of the edge between 2 and 3 that was placed in the previous auxiliary graph (Fig. 3b, left graph) is compared with the weight of the new edge. Given that both edge weights are equal to 1, the chosen edge cannot be decided by cost. Since the new edge does

©2017 Journal of Communications

not add a new lightpath, but rather upgrades an existing one, it is therefore selected to be placed in the auxiliary graph. Finally, the pair (1, 3) is analyzed. Taking into account that the total distance between the nodes is 2700 km, only the third format can be used between those nodes. By applying the algorithm, a new edge with weight 2 is added to the auxiliary graph between 1 and 3, and since there is no lightpath directly connecting nodes 1 and 3, the auxiliary graph construction terminates. The final auxiliary graph is shown in Fig. 3 b) right side. B. Heuristic Algorithm Description The heuristic algorithm is structured in two stages, a sequential grooming and routing of the demands and an iterative solution optimization with redistribution of demands. The pseudocode for the two stages is detailed below: ____________________________________________ Algorithm 2 Sequential Grooming and Routing of Demands _ INPUT: Graph G(N, L), traffic matrix T, set of transmission formats F. OUTPUT: List of lightpaths Lp. 1: 2: 3: 4: 5:

504

FOR every traffic demand tsd, Calculate the K-shortest paths, Psd-K using Yen´s algorithm [18]. FOR each Psd-K, Generate the auxiliary graph Gaux with Algorithm 1. Obtain and record the shortest path within the auxiliary graph, denoted here as the logical shortest path lsd, using the Bellman-Ford

Journal of Communications Vol. 12, No. 9, September 2017

6: 7:

8:

9: 10:

algorithm [19]. This logical path lsd corresponds to the combination of lightpath actions necessary to route the demand tsd with minimum cost. END FOR. Compare the cost of the logical paths lsd for the various K shortest paths Psd-K and choose the one that has the minimum cost, or in case of equal cost the one with the lowest spectrum usage (measured as the total number of slots required). Implement in the network the solution chosen for the current traffic demand tsd. This implies the creation, update or use of lightpaths lp, as well as the update of the number of frequency slots being used in every link of the path Psd-K chosen. Add the created/changed lightpaths lp to the list of lightpaths Lp. END FOR.

6:

7: 8:

9: 10: 11: 12: 13:

lp, while considering that the lightpath lp is not in the network. Calculate the logical shortest path lsd in the auxiliary graphs using the Bellman-Ford algorithm and save the best solution. END FOR. IF the cost of lightpath lp is higher than the cost of the new solutions for all of its demands tsd or if the cost is the same but the spectrum usage is higher THEN, Substitute lightpath lp with the new solutions generated in 6. END IF. END FOR. Repeat Steps 1 to 11 until no changes are made in the network for a given iteration. Calculate and return the total network cost and the spectrum utilized.

After applying the iterative stage, the heuristic algorithm terminates, and a solution for the grooming and routing problem is obtained in the form of a lightpath list. It is the closest solution this algorithm can generate to the optimum one. Along with the list of lightpaths, a final information report is compiled regarding the final network state. The report includes, between other data, the total network cost and the average link spectrum utilization, that will be analyzed in the following section.

The described stage of the algorithm offers a possible and valid solution to the grooming and routing problem in the form of the list of lightpaths. This solution is not yet optimized since the ordering of the demands can affect the grooming options. The next stage of the algorithm aims to minimize the network cost by re-evaluating the demand routing done by Algorithm 2. ______________________________________________ Algorithm 3 Iterative Demand Redistribution _ INPUT: Graph G(N, L), set of transmission formats F, list of lightpaths Lp. OUTPUT: Improved list of lightpaths Lp’, total network cost, spectrum used.

IV. RESULTS AND DISCUSSION A. Network and Traffic Models In this section the results obtained with the heuristic algorithm described previously are analyzed. The results were collected by varying the algorithm input values (traffic load, traffic distribution and grid type) one at a time, while maintaining the other values constant. For each variation, the algorithm was run for 10 independent simulations with different traffic matrixes and the average value computed. The impact on the results of each input variation was then examined and the value which produced the best results was from then on selected to use in the following variations.

1: Order the list of lightpaths Lp by ascending order of their occupation ratio. 2: FOR all lightpaths lp in the ordered list, 3: Calculate K shortest paths Plp-K between the source and destination nodes of the lightpath lp. 4: FOR all traffic demands tsd being carried by the lightpath lp, 5: Generate auxiliary graphs Gaux for traffic demand tsd for all of the K shortest paths Plp-K of

Fig. 4. Network topologies studied [20].

In the analysis, we considered two physical network topologies shown in Fig. 4: The GBN network (17 nodes and 26 links) and the CORONET network (75 nodes and 99 links). These topologies are intended to represent a

©2017 Journal of Communications

medium-sized metro/regional transport network and a large-sized long-haul one, respectively. All nodes have a structure similar to the one of Fig. 2, and are capable of traffic grooming. Each link is bidirectional (with two

505

Journal of Communications Vol. 12, No. 9, September 2017

unidirectional fibers) and each fiber is assumed to support 384 spectrum slots of 12.5 GHz. The pattern of connections is described by a mesh logical topology, i.e. the connections are randomly selected among the node pairs. For each demand the number of pre-computed paths (K) in the simulations is equal to 6. This value of K allows a good variety of path combinations to be analyzed while maintaining the running times of the algorithm in a relatively small value, in the order of minutes. The analysis is carried out for different traffic loads. The load values considered, given by the sum of client traffic demands offered to the network, are 10, 20, 30, 40, and 50 Tb/s. Depending on the load, the network becomes more or less congested and so the impact of altering the input variables can change. For each node pair, there are five types of traffic demands: ODU0, ODU1, ODU2, ODU3, and ODU4. The size of these demands can be computed using either a uniform distribution or a weighted one. In both cases the demands being generated are added up until the pre-defined load is reached. In the uniform distribution, all ODU types have the same probability of being generated, whereas in the weighted distribution the total volume of traffic is evenly distributed between all ODU types (i.e., the probability of a given ODU is inversely proportional to its bit-rate). In general the probability corresponding to an ODUk (k{0,1, 2, 3, 4}), P(ODUk), is calculated using the following equations:

requires not only a higher number of transceivers, but also those with higher optical reach, since this network has a greater number of nodes with longer links.

Fig. 5. Total network cost comparison for GBN and CORONET.

4

Fig. 6. Total Spectrum Usage Comparison for GBN and CORONET.

 P(ODUk )  1 ,

(1)

k 0

P(ODUk ) 

P(ODU0) , ( LODU0 / LODUk )

(2)

where LODUk is the total load that the ODUk can support. Table II displays the probabilities of each ODU type computed using the above equations. TABLE II: ODU PROBABILITIES FOR WEIGHTED DISTRIBUTION. ODUk

PODUk (%)

k=0

60

k=1

30

k=2

7.5

k=3

1.87

k=4

0.75

Fig. 7. Traffic distribution cost comparison.

B. Results Analysis Fig. 5 shows the total cost of the solution expressed in cost units (c. u.)for the two network configurations as a function of the total load. It can be observed that the cost increases with the traffic load, although the rate of increase is higher for the CORONET network than for the GBN one. This means that for a certain network cost the first network can support a lower total load than the second one. This is due to the fact that the CORONET topology ©2017 Journal of Communications

506

Fig. 8. Grid cost comparison.

Journal of Communications Vol. 12, No. 9, September 2017

Fig. 9. Grid Spectrum Usage Comparison.

Another metric considered in the analysis is the average spectrum usage calculated over all the network links. The spectrum usage is defined as the percentage of spectrum slots in the network assigned to lightpaths, and it is represented in Fig. 6 for various total traffic loads. It can be seen that the spectrum usage is always slightly lower (between 1.6% and 3%) for the CORONET network for the traffic loads analyzed. However, these results can be deceiving. In reality, in this network the traffic distribution is highly unbalanced with some links being heavily loaded and other links being almost empty. When a network has links with the entire spectrum used, it may lead to a bottleneck situation where it may not be possible to route the traffic demands. In those links, it becomes impossible to create or upgrade the lightpaths due to spectral capacity constraints. The fact that the maximum load possible in the CORONET network (≈ 30 Tb/s) is lower than in the GBN network, despite a higher amount of nodes and links in the former, supports this conclusion. Another question that deserves to be investigated is the impact of the traffic distribution in the total cost variation. This impact is described in Fig. 7 considering the GBN network, from which we can see that the weighted distribution leads to higher costs, between 25 % and 40 % higher than the uniform distribution. For what concerns the spectrum usage, the weighted distribution is slightly more demanding, since it requires between 0.25% and 1.5 % more spectrum than the uniform distribution, for the same traffic load. These results come from the fact that the uniform distribution generates a lower number of demands than the weighted one for the same total traffic load. This is a consequence of the fact that in this distribution both the demands that require large capacity and the ones that require smaller capacity have the same probability. With fewer demands, less lightpaths are created and the resources needed to implement the network can also be reduced. Finally, a comparison is made between the flexible-grid and fixed-grid DWDM solutions, considering both networks studied and the weighted traffic distribution. The main difference between these two solutions resides in the transmission formats utilized when running the algorithm. With the flexible-grid all the formats shown ©2017 Journal of Communications

507

in Table I are accounted for, while for the fixed-grid only the first and second formats are considered. The obtained results for the total cost are shown in Fig. 8. As can be seen, the flexible-grid scheme can reduce the total cost with savings ranging from 30% to 35% for GBN network and from 20% to 25% for CORONET network. In terms of spectrum usage the flexible-grid scheme also achieves some gains in comparison with the fixed-grid solution. In Fig. 9, spectral reductions between 5% to 20 % for the GBN network and 1.5 % to 5% for the CORONET network are visible. These results are a consequence of the greater flexibility in format selection, which allows to choose the best format for the different combinations of network links and traffic matrices. With these formats, it is possible to groom more demands in each lightpath, with a cost that is lower than creating a new one. The outcome is a network with less lightpaths. Although each lightpath can have a higher cost and a higher spectrum usage that the ones used in fixed-grid, the reduction of the number of lightpaths clearly outweighs these factors. Comparing these results with the ones published in literature, it can be recognized that our approach can obtain a reasonable improvement over previous solutions. In [13] a comparison is made between using fixed and flexible-grid using a variety of modulation formats. It is concluded that using 400Gb/s line rate modulation formats, it is possible to obtain on average a 15% cost reduction. In [14] similar conditions to ours were considered, but with different flexible-grid modulation formats. By developing a different heuristic algorithms, the authors were able to achieve a 10% reduction in cost in metro scenarios, where the optical reach limitations of 400Gb/s channels are less relevant. V. CONCLUSION In this paper, we presented a novel heuristic algorithm to solve the routing and traffic grooming problem in OTN over flexible-grid DWDM networks. The aim of the algorithm is to minimize the total network cost and spectrum usage, and takes as input different feasible configurations of transponders, a model for the network nodes that includes ODU switching and optical switching and a traffic matrix. The algorithm is based on the concept of an auxiliary graph and it is organized in two stages: a sequential and an iterative one. The first stage provides a solution for the problem in a form of a set of lightpaths and the second stages tries to improve the quality of the solution by rearranging the assignment of demands to lightpaths. Different network topologies and traffic distributions were evaluated in this context. Our results showed that larger networks with more nodes do not necessarily have increased overall capacity due to the effect of bottleneck links. Additionally, the impact of optical reach in these scenarios can significantly increase network cost. Finally, a flexible-grid scheme is shown to clearly outperform a fixed-grid one in terms of both cost and spectrum usage.

Journal of Communications Vol. 12, No. 9, September 2017

ACKNOWLEDGMENT This work was partially supported by the project UID/EEA/50008/2013 funded by Fundação da Ciência e Tecnologia e Instituto de Telecomunicações.

[14]

REFERENCES [1] ITU-T Recommendation G.709, “Interfaces for the Optical Transport Network (OTN),” 2012. [2] J. Santos, J. Pedro, P. Monteiro, and J. Pires, “Optimization framework for supporting 40 Gb/s and 100 Gb/s services over heterogeneous optical transport networks,” Journal of Networks, vol. 7, no. 5, pp. 783-790, May 2012. [3] ITU-T Recommendation G.694.1, “Spectral Grids for WDM Applications: DWDM Frequency Grid,” June 2012. [4] A. Lord, P. Wright, and A. Mitra, “Core Networks in the Flexgrid Era,” Journal of Lightwave Technology, vol. 33, no. 5, pp. 1126-1135, March 2015. [5] I. Tomkos, S. Azodolmolky, J. Solé-Pareta, D. Careglio, and E. Palkopoulou, “A tutorial on the flexible optical networking paradigm: State of the art, trends, and research challenges,” Proceeding of the IEEE, vol. 102, no. 9, pp. 1317-1337, June 2014. [6] A. Eira, J. Pedro, and J. Pires, “On the impact of optimized guard-band assignment for super channels in flexible-grid optical networks,” in OFC/NFOEC 2013, March 2013. [7] L. Velasco, A. Castro, M. Ruiz, and G. Junyent, “Solving routing and septrum allocation related optimization problems: From off-line to in-operation flexgrid network planning,” Journal of Lightwave Technology, vol. 32, no. 16, pp. 2780-2795, August 2014. [8] J. Pedro, J. Santos, and J. Pires, “Performance evaluation of integrated OTN/DWDM networks with single-stage multiplexing of optical channel data units,” in Proc. 13th International Conference on Transparent Otical Networks, June 2011. [9] M. Klinkowski and K. Walkowiak, “Routing and spectrum assignment in spectrum sliced elastic optical path network,” IEEE Communications Letters, vol. 15, no. 8, pp. 884-886, June 2011. [10] I. Katib and D. Medhi, “IP/MPLS-over-OTN-over-DWDM multilayer networks: An integrated three-layer capacity optimization model, a heuristic, and a study,” IEEE Transactions on Network and Service Management, vol. 9, no. 3, pp. 242-253, September 2012. [11] M. Bertolini, O. Rocher, A. Bisson, P. Pecci, and G. Bellotti, “Benefits of OTN switching introduction in 100 Gb/s optical transport networks,” OFC/NFOEC 2012, paper NM2F.2 March 2012. [12] A. Deore, O. Turkcu, S. Ahuja, S. J. Hand, and S. Melle, “Total cost of ownership for WDM and switching Archictectures for next-generation 100Gb/s networks,” IEEE Communications Magazine, vol. 50, no.11, pp. 179-187, November 2012. [13] A. de Sousa, C. Lopes, and P. Monteiro, “Design cost and spectrum efficiency comparison of fixed-grid and flex-grid optical networks with grooming,” presented at the 16th International Telecommunications Network Strategy and

©2017 Journal of Communications

508

[15]

[16]

[17]

[18]

[19] [20]

Planning Symposium, Funchal, Madeira, Portugal, September 17-19, 2014. A. Eira, J. Pedro and J. Pires, “Cost-Optimized Dimensioning of Translucent WDM Networks with Mixed-Line-Rate Spectrum-Flexible Channels,” in Proc. 13th International Conference on High Performance Switching and Routing, June 2012, pp. 185–190. P. Wright, A. Lord, and L. Velasco, “The network capacity benefits of flexgrid,” in Proc. 17th International Conference on Optical Network Design and Modeling, April 2013, pp. 7–12. H. Zhu, H. Zang, K. Zhu and B. Mukherjee, “A novel generic graph model for traffic grooming in heterogeneous WDW mesh networks,” IEEE/ACM Transactions on Networking, vol. 11, no. 2, pp. 285-299, April 2003. A. Martins, “Traffic grooming, routing and wavelength assignment in metropolitan transport networks,” M.S. thesis, Dep. Elect. & Comp. Eng., IST, Lisbon Uni., Lisboa,, Portugal, 2014. J. Y. Yen, “Finding the K shortest loopless paths in a network,” Management Science, vol. 17, no. 17, pp. 712– 716, July 1971. R. Bellman, “On a routing problem,” Quarterly of Applied Mathematics, vol. 16, pp. 82–90, 1958. A. Eira, J. Santos, J. Pedro, and J. Pires, “Multi-Objective design of survivable flexible-grid DWDM networks,” Journal of Optical Communications and Networking, vol. 6, no. 3, pp. 326-339, April 2014.

João R. Santos was born in Lisbon, Portugal, in 1990. He received the M. S. degree in electrical and computer engineering from Instituto Superior Técnico, University of Lisbon, Portugal, in 2015. He is currently a Software Developer for a multi-layer planning tool at Coriant Portugal. His research interests include optical transport networks and multi-layer network design and optimization.

António Eira was born in Lisbon, Portugal and received his MSc. In Electrical and Computer Engineering from Instituto Superior Técnico, Technical University of Lisbon in 2010. His research interests include routing and spectrum assignment algorithms, multi-layer planning and topology optimization for access and core networks. He has authored over 20 publications in international conferences and journals on optical networking and participated in the EU FP7 Integrated Project IDEALIST. He is currently working at the multi-layer optimization group at Coriant Portugal, and is also associated with Instituto de Telecomunicações (IT), Lisbon, Portugal.

Journal of Communications Vol. 12, No. 9, September 2017

João Pires received the Ph.D. degree in electrical and computer engineering from the Technical University of Lisbon, Portugal, in 1993. He is currently an Assistant Professor at Instituto Superior Técnico, University of Lisbon. He has lectured widely on optical communications and telecommunications networks. He has worked on a number of European-funded projects, including RACE, ACTS and FP7 projects. He is author or co-author of more than 100 papers in international journals and conference proceedings. At present his research interests are mainly in the area of optical transport and access networks and network design and optimization.

©2017 Journal of Communications

509