**6. Attack‐aware routing and wavelength assignment under demand uncertainty for multi‐period planning**

As emphasized above, multi‐period network planning is crucial in avoiding overprovisioning WSSs within hybrid nodes. As such, the aforementioned Aa‐RWA algorithm is extended in this section to consider the demand forecasts of future time periods and in doing so to ensure that the WSS placement considers the changing network characteristics. In line with the most popular period‐planning types available in the literature, the Aa‐RWA algorithm is applied for both the incremental network planning case as well as the multi‐period planning approach. In the former case, the Aa‐RWA algorithm is applied in each step, the WSS placement for that step is decided, and the subsequent period considers the presence of those WSSs in the network when running the Aa‐RWA algorithm for the next time period. In the multi‐period approach on the other hand, the in‐band and out‐of‐band interactions in each node are cal‐ culated for all time periods by the Aa‐RWA algorithm and then statistical measures are used to assess the level of interaction and the extent to which a WSS is needed at a specific node.

In either case, the level of in‐band and out‐of‐band interactions (and the subsequent decision on WSS placement) is strongly governed by the demand uncertainties and the assumptions made on growth year after year. The growth factor is assumed to be the mean value around a normally dis‐ tributed random variable of the actual traffic growth between source destination pairs and thus Monte Carlo simulations are conducted to investigate the overall performance under indepen‐ dent trials. Details of the network setup and the exact values considered are detailed in Section 7.

#### **6.1. Incremental Aa‐RWA network planning**

In incremental Aa‐RWA network planning, there is knowledge for the demand distribution for only one period at a time (the period under consideration). For this reason, decisions are taken only for the current period. The flowchart of the proposed algorithm is given in **Figure 6**. The algorithm takes as input *N* independent sets of demands. For each one of the *N* sets, the algorithm solves the problem according to the deterministic Aa‐RWA algorithm as presented in Section 5 and produces *N* outputs with metrics related to in‐band and out‐ of‐band interactions. These metrics associate two values for each input port of every network node. Specifically, these values count the number of lightpaths that interact though in‐band and out‐of‐band crosstalk in the specific input port. Based on these values, the algorithm specifies the ports where WSSs should be placed. The assumption in this work is that in every period a maximum number of *m* WSSs can be placed due to budget constraints. The input ports where the WSSs are placed are chosen according to the maximum mean values of the in‐band and out‐of‐band interactions. Subsequently, the output of each period contains the established lightpaths, and the next period takes as input the already established lightpaths and the placement of the WSSs from the previous period. The same procedure is followed for every period during the entire time horizon under consideration.

#### **6.2. Multi‐period Aa‐RWA network planning**

intra‐ and inter‐ channel crosstalk and thus to minimize the propagation of high‐power jam‐

The wavelength utilization *BWAV*p of the candidate pre‐calculated paths for the source‐destina‐

from the set of candidate lightpaths with the smallest number of in‐band and out‐of‐band channel interactions with the already established lightpaths, is chosen. To evaluate this, the wavelength

lightpath with the minimum sum of in‐band and out‐of‐band channel interactions is established.

connection is blocked. Subsequently, the algorithm establishes lightpaths for all the connec‐ tion requests in sequential order. The output of the algorithm is a set of established lightpaths in terms of paths and wavelengths. For each lightpath, the algorithm also returns two scalars that represent the number of inter‐channel and the intra‐channel interactions of this lightpath

**6. Attack‐aware routing and wavelength assignment under demand** 

As emphasized above, multi‐period network planning is crucial in avoiding overprovisioning WSSs within hybrid nodes. As such, the aforementioned Aa‐RWA algorithm is extended in this section to consider the demand forecasts of future time periods and in doing so to ensure that the WSS placement considers the changing network characteristics. In line with the most popular period‐planning types available in the literature, the Aa‐RWA algorithm is applied for both the incremental network planning case as well as the multi‐period planning approach. In the former case, the Aa‐RWA algorithm is applied in each step, the WSS placement for that step is decided, and the subsequent period considers the presence of those WSSs in the network when running the Aa‐RWA algorithm for the next time period. In the multi‐period approach on the other hand, the in‐band and out‐of‐band interactions in each node are cal‐ culated for all time periods by the Aa‐RWA algorithm and then statistical measures are used to assess the level of interaction and the extent to which a WSS is needed at a specific node.

In either case, the level of in‐band and out‐of‐band interactions (and the subsequent decision on WSS placement) is strongly governed by the demand uncertainties and the assumptions made on growth year after year. The growth factor is assumed to be the mean value around a normally dis‐ tributed random variable of the actual traffic growth between source destination pairs and thus Monte Carlo simulations are conducted to investigate the overall performance under indepen‐ dent trials. Details of the network setup and the exact values considered are detailed in Section 7.

In incremental Aa‐RWA network planning, there is knowledge for the demand distribution for only one period at a time (the period under consideration). For this reason, decisions

of the links. For each demand, the lightpath (*p*, *w*),

. If there are no available wavelengths, then the

is updated. The algorithm at

are used to identify the interactions of established lightpaths. Then, the

ming signal attacks.

58 Optical Fiber and Wireless Communications

availability vectors *BWAV*<sup>l</sup>

tion pair (*s*, *d*) is computed based on the *BWAV*<sup>l</sup>

each step establishes a requested connection *Λsd*

**uncertainty for multi‐period planning**

**6.1. Incremental Aa‐RWA network planning**

with the other established lightpaths.

After establishing the lightpath (*p*, *w*), the corresponding *BWAV*<sup>l</sup>

In multi‐period Aa‐RWA network planning, there is a priori knowledge for the demand dis‐ tribution for all the time periods under consideration. Therefore, decisions are taken based on the traffic estimate for all time periods. The flowchart of the proposed algorithm is given in **Figure 7**. The algorithm takes as input *N* independent sets of demands for every one of the *T* periods (increasing over time based on a multiplicative factor as previously mentioned).

**Figure 6.** A flowchart of the incremental Aa‐RWA algorithm.

**Figure 7.** A flowchart of the multi‐period Aa‐RWA algorithm.

For each one of the *N* sets and for each time period, the algorithm solves the problem accord‐ ing to the deterministic Aa‐RWA algorithm and produces *N\*T* outputs with metrics related to the in‐band and out‐of‐band interactions. Based on these values, the "multi‐period WSSs placement" module specifies the input ports and the time periods for the placement of the WSS. Again, the assumption is that in every period, a maximum number of *m* WSSs can be placed due to budget constraints. In this case, the placement of the WSSs is performed based on the maximum mean values of the in‐band and out‐of‐band interactions over all instances and all periods.

#### **7. Performance results**

The network topology used in our simulations was the Geant‐2 network topology [17] that has 34 nodes and 54 bidirectional links (108 fibers; shown in **Figure 8**). Each fiber is able to

**Figure 8.** Geant‐2 network topology: 34 nodes, 54 links.

For each one of the *N* sets and for each time period, the algorithm solves the problem accord‐ ing to the deterministic Aa‐RWA algorithm and produces *N\*T* outputs with metrics related to the in‐band and out‐of‐band interactions. Based on these values, the "multi‐period WSSs placement" module specifies the input ports and the time periods for the placement of the WSS. Again, the assumption is that in every period, a maximum number of *m* WSSs can be placed due to budget constraints. In this case, the placement of the WSSs is performed based on the maximum mean values of the in‐band and out‐of‐band interactions over all instances

The network topology used in our simulations was the Geant‐2 network topology [17] that has 34 nodes and 54 bidirectional links (108 fibers; shown in **Figure 8**). Each fiber is able to

and all periods.

**7. Performance results**

60 Optical Fiber and Wireless Communications

**Figure 7.** A flowchart of the multi‐period Aa‐RWA algorithm.

support 80 wavelengths. The capacity of each wavelength was assumed equal to 10 Gbps. Initially, 50 different traffic matrices were produced with uniform distribution between source destination pairs and mean value equal to 1.35 Tbs of total requested capacity. Both algorithms (multi‐period Aa‐RWA and incremental Aa‐RWA) were studied for five periods. The growth factor for each period was assumed to be equal to 1.5. The demand increase for each period applies for the source destination pairs that have a non‐zero value at the initial traffic matrix. The algorithms for each source destination pair computed *k* = 3 alternative can‐ didate paths.

In **Figure 9**, results for the multi‐period Aa‐RWA algorithm are depicted. Specifically, in **Figures 9(a)**, **(b)**, the mean values for inter‐channel and intra‐channel crosstalk for a horizon of five periods are presented, respectively. The mean values are the result of the 50 different traffic matrices. The inter‐channel and intra‐channel crosstalk per link (input port of a node) are the number of the interactions at this port. In **Figure 9**, the central mark of each box is the median, and the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme data points that are not considered outliers, and outliers are plotted individually.

**Figure 9.** Mean values of multi‐period Aa‐RWA algorithm for (a) inter‐channel crosstalk and (b) intra‐channel crosstalk for a horizon of five periods.

Both inter‐ and intra‐channel crosstalk increase exponentially with increasing traffic demands. However, as shown in **Figures 9(a)**, **(b)**, specific links experience significantly higher crosstalk than others. Therefore, the required WSSs can be placed only at the input ports of the nodes that experience high crosstalk.

Incremental Aa‐RWA algorithm follows the same trend as the multi‐period Aa‐RWA algorithm (as illustrated in **Figure 10**). Note that the trend would be completely different in the case where an attack‐unaware RWA algorithm was used. In that case, all the periods would experience high values of crosstalk as can be found from the results of [8]. These results are not presented here, since the scope of this chapter is to plan an optical network in order to deal with physical layer attacks and therefore an attack‐unaware RWA algorithm is out of the scope of this study.

**Figure 10.** Mean values of incremental Aa‐RWA algorithm for (a) inter‐channel crosstalk and (b) intra‐channel crosstalk for a horizon of five periods.

In **Figure 11**, the mean value of inter‐ and intra‐channel crosstalk that the links experience during time period 5 is presented for the multi‐period Aa‐RWA algorithm. The results are presented in the form of histograms, where each column represents the number of links that have crosstalk between the ranges that are depicted in the x‐axis of the histograms. From **Figure 11**, it is clear that a very small number of links have very high crosstalk, while the majority of links experience only a small crosstalk effect. This result offers a good indication that an addition of a small number of WSSs at the specific nodes where high crosstalk is expe‐ rienced will significantly improve the performance of the network, thus minimizing the effect of a jamming attack. Note that the larger the number of links that appear in the leftmost bar, the smaller the crosstalk effect at the input ports of these nodes. Therefore, the best algorithms will be those where their histograms are more left shifted.

In **Figure 12**, the same histograms are presented for the case of the incremental Aa‐RWA algorithm. Compared to the previous results of the multi‐period case, the crosstalk effect of the incremental updating results to slightly increased inter‐channel crosstalk and comparable intra‐channel crosstalk. Nevertheless, the same crosstalk trends are observed here as well, where a small number of links experience significant crosstalk, while the rest of the links experience significantly lower crosstalk.

Both inter‐ and intra‐channel crosstalk increase exponentially with increasing traffic demands. However, as shown in **Figures 9(a)**, **(b)**, specific links experience significantly higher crosstalk than others. Therefore, the required WSSs can be placed only at the input ports of the nodes

**Figure 9.** Mean values of multi‐period Aa‐RWA algorithm for (a) inter‐channel crosstalk and (b) intra‐channel crosstalk

Incremental Aa‐RWA algorithm follows the same trend as the multi‐period Aa‐RWA algorithm (as illustrated in **Figure 10**). Note that the trend would be completely different in the case where an attack‐unaware RWA algorithm was used. In that case, all the periods would experience high values of crosstalk as can be found from the results of [8]. These results are not presented here, since the scope of this chapter is to plan an optical network in order to deal with physical layer attacks and therefore an attack‐unaware RWA algorithm is out of the scope of this study.

**Figure 10.** Mean values of incremental Aa‐RWA algorithm for (a) inter‐channel crosstalk and (b) intra‐channel crosstalk

that experience high crosstalk.

62 Optical Fiber and Wireless Communications

for a horizon of five periods.

for a horizon of five periods.

In **Figure 13**, the total number of required WSSs in order to minimize the impact of crosstalk effect per period is presented for the two proposed algorithms. For each period, the algo‐ rithms decide to place a WSS at the input port of a link when the mean values of the inter‐ and intra‐channel crosstalk are above a certain threshold. Based on these decisions, the multi‐ period Aa‐RWA algorithm requires less number of WSSs per period as compared to the incre‐ mental Aa‐RWA algorithm. This is due to the fact the routing and wavelength assignment of the multi‐period algorithm takes into account the future traffic demands, and the decisions are more appropriate. On the other hand, the incremental algorithm may decide to place a WSS in one period, and in future periods, there will be demands that would not be able to be established over already placed WSSs due to insufficient number of wavelengths. Thus, there would be not enough choices for efficient routing and wavelength assignment.

**Figure 11.** Histogram for link (input ports of nodes) distribution related to (a) inter‐channel and (b) intra‐channel crosstalk interactions for multi‐period Aa‐RWA algorithm for the fifth period.

**Figure 12.** Histogram for link (input ports of nodes) distribution related to (a) inter‐channel and (b) intra‐channel crosstalk interactions for incremental Aa‐RWA algorithm for the fifth period.

**Figure 13.** Number of required WSSs per period for the incremental Aa‐RWA and the multi‐period Aa‐RWA algorithms.

#### **8. Conclusions**

This chapter proposed new attack‐aware RWA algorithms for the multi‐period planning of opti‐ cal networks under demand uncertainty. These algorithms decide on the placement of wave‐ length selective switches at the input ports of network nodes and the period that the placement should be performed. The decisions are taken based on the distribution of the demands with the objective to minimize the impact of physical layer attacks over all periods. The algorithm that takes into account jointly all the time periods has a better performance than the algorithm that takes into account the periods in a sequential manner, resulting in a smaller number of required WSSs to be placed in the network so as to minimize the effect of a jamming attack.
