## 6 The Experiment
It is possible to add more traffic patterns as attractors, but we limited the number of traffic patterns to six in this experiment for a more clear presentation of attractor dynamics. For each of the selected traffic patterns, we pre-computed the VNT configurations and routing tables of the network slices optimized for the traffic scenario and stored them in our application along with the corresponding BAM attractor. As an example, the VNT configuration of network slices optimized for the traffic pattern in Figure 4 is shown in Figure 6a. VNT configuration of another  
Figure 5: The traffic rate of sensors after the crowd moved to the edge of core node 5.  
pattern in Figure 5 is shown in Figure 5. In the network, the traffic of sensor type 1 was assigned to network slice 1, and the traffic of sensor type 2 was assigned to network slice 2. To minimize the maximum link utilization and maximize the power efficiency in the experiment, we optimized the VNT configuration in two steps. In the first step, for a selected traffic pattern, the set of routing tables at each network slice that minimizes the maximum link utilization in the physical network was found. Then, among the solutions of the first step, a solution that uses the minimum number of physical links when routing the aggregate traffic is selected to minimize the power consumption as the links that are unused in all slices can be set to idle. For example, four physical links in Figure 5 were set to idle because none of the VNTs used them. While VNT was the same in both slices in Figure 5, the slices had different routing tables to minimize the overall maximum link utilization of the physical network. For example, the traffic from the edge of core node 1 to the edge of core node 9 followed the path 1\(\rightarrow\)9 on slice 1, while the traffic between the same edge nodes followed the path 1\(\rightarrow\)2\(\rightarrow\)8\(\rightarrow\)9 on slice 2 as shown with arrows. The VNTs on slices may also differ depending on the placement of sensors and the traffic matrix on each slide. The edge link traffic rate statistics fetched by our application via the northbound of SDN controller in the experiment are shown in Figure 7. The edge links are numbered according to the number of the core node that they are connected to. The traffic of sensors started in the first 3 s. The first edge link traffic statistic were fetched at time 3 s. Initially, the crowd was around the sensors connected to core 1, so those sensors were producing the highest traffic and the edge link 1 had the highest link utilization as seen in Figure 4. Figure 8 shows the traffic rate on the link from core node 6 to core node 9 and the link from core node 1 to core node 9, which are the most congested core links in the network. Initially, the network configuration was not optimized for this traffic pattern, so the link from core node 1 to core node 9 started to carry high amount of traffic at time 10 s. The BAM algorithm in our application tried to identify the traffic pattern by using the traffic rate statistics of  
Figure 6: The VNTs in the network slices and the active/idle link configuration in the physical topology optimized for the initial traffic matrix when the crowd was close to **(a)** the edge of core node 1 and **(b)** the edge of core node 5.  
the edge links. Each time a new observation of statistics was fetched, BAM updated the confidence level of the attractors. The confidence level of the attractors calculated by BAM during the experiment is shown in Figure 3.0 Initially, the confidence level of all attractors were zero. After getting only two traffic statistics, the confidence level of attractor 1 became the highest and the confidence difference between the highest and the second highest attractors passed the threshold \(\lambda\) set to 0.5, so attractor 1 was correctly identified as the new state.