The PDR algorithm vs. ACR algorithms

In this section, the performance of PDR v.1 and PDRv.2 are evaluated based on some test scenarios and discuss the results [25], [26] and [27]. The AntNet and TB algorithms are modified, by replacing the transmission delay with the available BW information to be able to compare the PDR algorithm with them. In section 5.6.1, the simulation details are presented. In section 5.6.2, the MIRA topology is considered and the performance of the compared algorithms is tested. In section 5.6.3, the Internet2 topology is considered and the performance of the compared algorithms is tested.

5.6.1 The simulation details

The simulation details are presented in the following points:-

 Simulation workflow:-

o Three performances parameters are measured:

o The rejection ratio of requests and o The bandwidth blocking rate.

o The effect of prediction use.

o This experiment uses the same procedure in section 5.2 in order to focus on the steady state of network traffic.

o This experiment uses the same procedure of the analysis study in section 5.4 in order to select the best values for the parameters of PDR algorithm, (i.e. ETh and α), in all tested scenarios.

 The parameters of PDR algorithm:-

Table 5.10 describes the parameters of PDR algorithm and shows the used value in this simulation. Table 5.11 summarizes the result of analysis studies in Appendix A.3 that are performed in order to select the best values of Eth and α parameters for the PDR algorithm.

Table ‎5.10 The parameters of PDR algorithm.

Variable Value

lc (least interference control parameter) 0.1 M (keep the average of the last M of td) 15

δ (learning rate) 0.01

θ (congestion weight) 0.25

WS (window size) 1

Table ‎5.11 The best values of ETh and α and parameters (PDR).

Algorithm MIRA Internet2

ML HL

ETh 60 54 1800

α 0.6 0.5 0.25

5.6.2 The MIRA topology

In the following scenarios, the MIRA topology is considered and the performance of routing algorithms is tested in both ML and HL scenarios.

5.6.2.1 The ML scenario

Figure 5.39 shows the rejection ratio of requests for the ML scenario. The average of results shows that, the PDRv.2 algorithm rejects 38.76% less requests than PDRv.1 algorithm, 46.17% less requests than TB algorithm and 63.40% less requests than AntNet algorithm. However, the PDRv.1 algorithm rejects 12.10%

less requests than the TB algorithm and 40.24% less requests than the AntNet algorithm.

Figure ‎5.39 The rejection ratio of requests for the ML scenario.

The AntNet algorithm is considered the first algorithm that is inspired by ant colony behavior to solve the routing problem. However, The TB algorithm, which

is an advanced ant-based routing algorithm, is meant to be an extension of existing link-state protocols such as OSPF, which provides shortest-path information to initialize the probability table. Therefore, TB does not require a learning period to discover the network topology.

The experiment result shows that, the PDR mechanism is an effective approach which combines the current available BW and the predicted available BW in order to determine the amount of pheromone to deposit. Additionally, the proposed predictor uses an adaptive mechanism to be able to locally adapt the prediction validity period depending on the prediction accuracy in order to efficiently predict the link traffics.

The PDRv.2 algorithm outperforms the PDRv.1 algorithm because the PDRv.2 algorithm uses a new adaptive Ant-based mechanism to be able to efficiently distribute the ants on the network topology and accurately discover the best paths.

Additionally, the used Ant-based mechanism is incorporated with a new efficient prediction approach, which uses the dynamic FFNN instead of the static FFNN that is used in the previous version.

Figure 5.40 shows the bandwidth blocking rate for the moderate load scenario.

The average of results shows that, the PDRv2 algorithm rejects 38.34% less BW than PDRv.1 algorithm, 44.97% less BW than the TB algorithm and 62.37% less BW than the AntNet algorithm. However, the PDRv.1 algorithm rejects 10.75%

less bandwidth than the TB algorithm and 38.96% less bandwidth than the AntNet algorithm.

Figure ‎5.40 The bandwidth blocking rate for the ML scenario.

5.6.2.2 The HL scenario

Figure 5.41 shows the rejection ratio of requests for the HL scenario. The average of results shows that, PDRv.2 algorithm rejects 29.04% less requests than PDRv.1 algorithm, 33.77% less requests than TB algorithm and 45.82% less requests than AntNet algorithm. However, PDRv.1 algorithm rejects 6.66% less requests than TB algorithm and 23.65% less requests than AntNet algorithm.

Figure ‎5.41 The rejection ratio of requests for the HL scenario.

Figure 5.42 shows the bandwidth blocking rate for the HL scenario. The average of results shows that, PDRv.2 algorithm rejects 27.78% less BW than PDRv.1 algorithm, 32.06% less BW than TB algorithm and 44.55% less BW than AntNet algorithm. However, PDRv.1 algorithm rejects 5.93% less bandwidth than TB algorithm and 23.23% less bandwidth than AntNet algorithm.

Figure ‎5.42 The bandwidth blocking rate for the HL scenario.

5.6.3 Real traffic scenario

Figure 5.43 shows the rejection ratio of requests for the real traffic scenario.

Based on the results, PDRv.2 algorithm rejects 7.30% less requests than PDRv.1 algorithm, 18.80% less requests than TB algorithm and 29.64% less requests than AntNet algorithm. However, the PDRv.1 algorithm rejects 12.41% less requests than the TB algorithm and 24.10% less requests than the AntNet algorithm.

Figure ‎5.43 The rejection ratio of requests for the real traffic scenario.

Figure 5.44 shows the bandwidth blocking rate for the real traffic scenario. Based on the results, PDRv.2 algorithm rejects 6.13% less BW than PDRv.1 algorithm, 4.21% less BW than the TB algorithm and 11.47% less BW than the AntNet algorithm. However, the PDRv.1 algorithm rejects 4.17% less bandwidth than the TB and 5.68% less bandwidth than the AntNet algorithm.

Figure ‎5.44 The bandwidth blocking rate for the real traffic scenario.

5.6.4 The effect of prediction use

Table 5.12 shows the enhanced performance for the rejection ratio of requests (%) depending on the prediction use. In this section, we aim to study the effect of prediction use. Therefore, we run the PDRv.1 and PDRv.2 algorithms one time without the prediction use (α=0) and another time with the prediction use.

In general, the prediction use within the PDRv.2 algorithm has a positive impact on the routing performance more than the prediction use within PDRv.1 algorithm. The main reason for this enhancement is the new structure of used dynamic FFNN.

Table ‎5.12 The enhanced performance depending on the prediction use.

Network Load scenario

PDRv.1 algorithm

PDRv.2 algorithm ML scenario _{6.27 (%) 8.37 (%)} HL scenario _{3.32 (%) 4.20 (%)}