## 6 The Experiment
The attractor identified as the current traffic pattern by BAM is shown in Figure 3.1 The attractors number 1 and number 5 denote the traffic patterns when the crowd is around the edge sensors of core node 1 and core node 5, respectively. The time when BAM identified a new traffic pattern is denoted by IT (Identified Traffic) and shown with blue dashed lines in the experiment results. Figure 3.2 The traffic rate on the links from core node 6 to core node 9 and core node 1 to core node 9. Figure 3.3 The edge link traffic rate statistics fetched by our application from OpenDaylight. Figure 3.4 The confidence level of the attractors. After identifying the new traffic pattern, our SDN application sent the VNT and routing table configuration optimized for traffic pattern 1 to the VTN coordinator by using its REST API. The VTN coordinator translated the configuration request of the application, updated its VTN configuration database, and sent it the OpenDaylight controller via the VTN manager. Finally, the OpenDaylight controller applied the new configuration to the SDN network by updating the flow tables of the switches by OpenFlow. This network reconfiguration process took around 10 s and finished at time 20 s, which is denoted by RF (Reconfiguration Finished) and shown with green dashed lines in the experiment results. After RF, the network started to run using a configuration optimized for this identified traffic matrix, so the maximum link utilization in the network decreased as seen in Figure 3.5  
Later, the crowd moved near the edge sensors of core node 5 and the traffic rate of the sensors changed as shown in Figure 3.6 As a result, there was an abrupt change in the traffic of edge and core links at time 58 s, which is denoted by TC (Traffic Changed) and shown with red dashed lines in the experiment results. The traffic rate on the edge link 5 became the highest as seen in Figure 3.7 The utilization of core link from node 9 to node 1 increased around two times as seen in Figure 8 because the network configuration was not optimized for the new traffic matrix. After the traffic change, the confidence level of attractor 1 started to decrease and the confidence level of attractor 5 started to increase as seen in Figure 3.8 At time 70 s, the confidence of attractor 5 passed that of attractor 1, and the BAM identified the traffic pattern of attractor 5 as the new traffic pattern as seen in Figure 3.9 Therefore BAM required fetching edge link traffic statistics only 5 times to identify the new traffic matrix. After identifying the new traffic pattern, our SDN application reconfigured the network to a configuration optimized for the identified traffic matrix, which took around 10 s and finished at time 80 s. After the reconfiguration finished, the maximum link utilization in the network decreased by two times. We repeated the traffic changes by moving the crowd to near the edge sensors of core node 1 at time 125 and then core node 5 at time 188 s. Again, BAM correctly identified the new traffic matrices after 5 iterations of fetching traffic statistics and our framework decreased the maximum link utilization by two times by reconfiguring the network via SDN. Our SDN application was run on a laptop with an Intel i7-3820 QM CPU. Even though our BAM implementation was not so optimized, the application used less than 10% of the single core of the laptop CPU produced in year 2012, which can be considered as a low CPU usage. Due to the budget constraints we couldn't set up a bigger testbed and we couldn't find a reference in the literature that evaluates the processing requirements of BAM, so the processing requirements of BAM on larger networks is not clear. In case the computation cost of the BAM becomes high, it may be decreased by replacing UKF with EKF, which is known to have a lower computational cost than UKF [47; 48], in the BAM implementation. [PERSON] et al. [8] says that they used the UKF in the BAM, because it provides a suitable tradeoff between the precision and computational efficiency. However, [PERSON] et al.