## 5 Discussion  
Analyzing the first approximation of the graphs of functions \(D_{s}~{}=~{}f(C)\) shown in Figure 3 for three virtual network segments \((s_{1},s_{2},s_{3})\), which functioned for a censored time in the information environment of the studied ecosystem of the 5 Ge base station, we can state that the probability of the loss of input requests decreases with increasing maximum available amounts of system resources. However, considering that the degree of a load of all virtual network segments was the same (the intensity of receipt and service of incoming requests were equal: \(\lambda~{}=~{}\{30,30,30\}\), \(\mu~{}=~{}\{30,30,30\}\)), but the distribution of guaranteed amounts of available system resources between segments was uneven (\(\overline{R}~{}=~{}\{225,150,75\}\) Mbps), then the initial value and rate of decrease in the graphs of the functions \(D_{s}~{}=~{}f(C)\) with the increasing importance of the argument turned out to be different. The connection is obvious--the greater the guaranteed amount of available system resources for a particular virtual network segment, the lower the inherent probability of losing the incoming request was. The nonlinear nature of the graphs of the functions shown in Figure 3 correlates with the analytical representation of the mechanism to control the allocation of system resources in the e-commerce-oriented ecosystem of the 5 Ge base station (see Section 3.4). The underlying optimization task with objective function (4) and constraints (5) and (6) was classified as nonlinear. At the same time, the descending nature of the graphs of the functions \(D_{s}~{}=~{}f(C)\), \(s~{}=~{}\overline{1,3}\), unambiguously confirms the effectiveness of the operation of the just mentioned control mechanism. The graphs of functions \(\overline{R}_{s}~{}=~{}f(C)\), \(s~{}=~{}\overline{1,3}\), which are presented in Figure 4 in the order of dynamics of increasing their values by increasing value of the argument, can be arranged as follows: \(\overline{R}_{3}~{}=~{}f(C)\), \(\overline{R}_{2}~{}=~{}f(C)\), \(\overline{R}_{1}~{}=~{}f(C)\). Again, this order correlates with the initial distribution of guaranteed amounts of available system resources between virtual network segments (\(\overline{R}~{}=~{}\{225,150,75\}\) Mbps). The example of graphs of functions \(\overline{R}_{3}~{}=~{}f(C)~{}\overline{R}_{2}~{}=~{}f(C)\) clearly shows the operation of the control mechanism for the allocation of system resources. It does not allow the virtual network segments to be overloaded, even in the conditions of a steady stream receiving new incoming requests. Figure 5: Functional dependence \(\Delta T_{s}=f(C)\) for the studied ecosystem of the 5 Ge base station. This is the responsibility of the system audit service and is based on the functional algorithm of the forced termination of the active session of terminal-segment information interaction for the overloaded virtual network segment (see Section 3.3). Finally, the growing nature of the graphs of the functions \(\Delta T_{s}\ =\ f(C),s\ =\ \overline{1,3}\) from Figure 5 indicates the need for this algorithm. Recall that \(\Delta T_{s}\) is the total relative (to the entire censored period of operation of the studied system) duration that the \(s\)-th virtual network segment was in the overload state, %. As the value of the argument, i.e., the total amount of system resource involved, increases and as the intensities of new incoming requests and the processing of received incoming requests are the same for all virtual network segments, there is a tendency to overload them rapidly. Moreover, the third segment \((\Delta T_{3}\ =\ f(C))\) suffers the most because of the primary guaranteed amount of allocated system resources, the smallest being \((\overline{R}_{3}\ =\ 75\) Mbps). However, let us compare the graphs of the functions shown in Figures 3 and 5.