## 6 Fuzzy Based Adaptive Resource Allocation
### Experimental Results and Discussions  
For all the experiments undertaken, an identical average inter-arrival time of 1.25 s (\(\lambda_{l}=\lambda_{2}=\lambda_{3}=\)0.8) are used for the three traffic profiles while the instantaneous load was varied by using different average service lifetimes such that the ratio \(\rho\) proportional to the load given by \(\rho_{i}=(bw_{i}\lambda_{i}/B_{i}\pi_{i})\,|\,\forall i\in[1,2,3]\) was varied from 0.2, 0.4, 0.6 to 0.8. Recall from the traffic profiles that \(bw_{l}=32\) kbps, \(bw_{2}=384\) kbps and b\(w_{3}=256\) kbps; thus for \(\lambda_{l}=\lambda_{2}=\lambda_{3}=0.8\) and the given B\({}_{1}\), B\({}_{2}\) and B\({}_{3}\) values in the policies the average service times used for each class are given in Table 2. Using these parameters, we investigated _service availability, blocking probability and bandwidth utilization_ obtained from admission control utilizing the fuzzy-based resource allocation. In order to benchmark the performance obtainable from the traffic profiles used in studying the implemented fuzzy-based scheme, we considered a scenario where an arriving flow is granted admission regardless of its class of traffic. Note that since identical average arrival rates are used for the three classes, this scenario is expected to yield maximal total service availability for every \(\rho\) considered (since admission is made regardless of class based upper limit); hence appropriate for comparative analysis with the implemented fuzzy-based scheme. For the purpose of comparison, we will call thisscenario the \"_Class-agnostic_\" scenario. Another scenario with admission control based on the fixed B\({}_{i}\) thresholds stipulated in the policies (_i.e._, without fuzzy-rule based resource allocation) is also analyzed. For the purpose of comparison, we will call this scenario the \"_Base Policy_\" scenario. After 10 experimental runs for each of the three scenarios with each \(\rho\) value given in Table 2, the average yielded results for the performance metrics depicted in Figures 13 and 14. Each experimental run had a total of 15000 arrivals request with voice, video and data class traffic each having 5000 generated service requests. From Figure 13, it can be seen that the FRB implementation allows better overall service availability for all the considered \(\rho\) representing a range of loading from low to high. This translates to potential higher revenue for an operator utilizing the fuzzy-rule based approach. As expected, service availability drops with higher traffic loading in all three scenarios. The probabilities of denied service request (blocking probabilities) per class are shown in Figures 14, 15 and 16. That of voice traffic can be seen in Figure 14 where the FRB scheme allows maximum service availability for voice traffic with \(\rho=0.2\) but about only 4% of the voice requests were denied with \(\rho=0.4\) compared to 15% when requests are granted without consideration for class or application of the FRB allocation. The implemented FRB resource allocation scheme is seen to improve the voice service request from Figure 14 over the Class-agnostic admission even at higher \begin{table}
\begin{tabular}{l c c c c} \hline \(\rho\) & **0.2** & **0.4** & **0.6** & **0.8** \\ \hline \(1/\pi_{I}\)(s) & 15 & 30 & 45 & 60 \\ \(1/\pi_{2}\)(s) & 1.6665 & 3.333 & 5 & 6.666 \\ \(1/\pi_{3}\)(s) & 1.875 & 3.75 & 5.625 & 7.5 \\ \hline \end{tabular}
\end{table} 
Table 2: Average service lifetime values (seconds) per class utilized in the experiments. Figure 12: A CNQF management UI screenshot showing traffic load for the three classes. traffic loading. The reason why the Base Policy scenario performs more favorably for the voice traffic as \(\rho\) is increased is because of the relatively low bandwidth requirement which reduces its overall probability of not obtaining enough bandwidth for its session whilst the more bandwidth hungry video and data class flows have been curtailed by the base policy thresholds.