## 3 Methodology and Proposed Innovation
### PA Properties of QUAKE
Regardless of the density of the underlying topology or the intensity of the incoming VNRs, Wii combinations would be formulated and applied on a case-by-case basis, unbound by any architectural and technological specifications. By also incorporating the algebraic characteristics of PA, the QUAKE framework proves to be scalable by design. The identification of the VNR's demands and its consequent classification to one of those combination categories is a key element for the success of the framework. The identification of the exact Wii combination to be applied and its governance can be a matter of either deterministic frameworks or intelligent managing systems. The integration of AI/ML systems with the Wii mechanism is beyond the scope of this work. \begin{table}
\begin{tabular}{c|c c c c|c c c c} \hline \hline
**Combinations** & **w1** & **w2** & **w3** & **w4** & **Combinations** & **w1** & **w2** & **w3** & **w4** \\ \hline
**1** & 0.25 & 0.25 & 0.25 & 0.25 & **13** & 0.8 & 0.15 & 0.025 & 0.025 \\
**2** & 0.3 & 0.3 & 0.2 & 0.2 & **14** & 0.7 & 0.25 & 0.025 & 0.025 \\
**3** & 0.4 & 0.4 & 0.1 & 0.1 & **15** & 0.1 & 0.1 & 0.5 & 0.3 \\
**4** & 0.4 & 0.3 & 0.2 & 0.1 & **16** & 0.35 & 0.6 & 0.025 & 0.025 \\
**5** & 0.3 & 0.4 & 0.2 & 0.1 & **17** & 0.2 & 0.7 & 0.05 & 0.06 \\
**6** & 0.3 & 0.3 & 0.3 & 0.1 & **18** & 0.2 & 0.2 & 0.1 & 0.5 \\
**7** & 0.5 & 0.3 & 0.1 & 0.1 & **19** & 0.05 & 0.05 & 0.6 & 0.3 \\
**8** & 0.5 & 0.2 & 0.2 & 0.1 & **20** & 0.2 & 0.25 & 0.5 & 0.05 \\
**9** & 0.2 & 0.3 & 0.3 & 0.2 & **21** & 0.25 & 0.2 & 0.1 & 0.45 \\
**10** & 0.2 & 0.2 & 0.3 & 0.3 & **22** & 0.25 & 0.25 & 0.1 & 0.4 \\
**11** & 0.15 & 0.1 & 0.55 & 0.2 & **23** & 0.15 & 0.15 & 0.4 & 0.3 \\
**12** & 0.8 & 0.1 & 0.05 & 0.05 & **24** & 0.025 & 0.025 & 0.7 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Produced Wii combinations for the examined applications. \begin{table}
\begin{tabular}{c c} \hline \hline
**Classification** & **Thresholds** \\ \hline
**Very high** & \(\geq\)60\% \\
**High** & 40-59\% \\
**Moderate** & 20-39\% \\
**Low** & 10-19\% \\
**Very low** & \(<\)10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification of Wii influence threshold groups. Here, an original classification scheme is proposed by interpreting the well-established thresholds researched throughout the corresponding literature into suitable Wii combination groups. Depending on the examined scenario, Algorithm 1 determines the Wii groups, by recognizing the grade of influence each primary performance index should have on the QUAKE composite metric. The procedure leads to tailored performance-oriented resource embeddings for demanding user-centric applications. The Wii classification scheme can be seamlessly utilized by any orchestrating authority, due to its technological-independent design. ```
SN \(\leftarrow\) G(\(V,E\)) init.VNR.DB(VNR[]) init.thres.groups(Wii[]) whileIS_EMPTY(VNR[])) \(\
eq\)\(TRUIE\)do whileIS_EMPTY(Wii[])) \(\
eq\)\(TRUIE\)do \(\mathrm{VNR_{c}[x,y]}\)\(\leftarrow\)\(\mathrm{VNR[x]}\),\(\mathrm{Classify(VNR[x],Wii[y])}\) \(y\gets y+1\) endwhile \(x\gets x+1\) endwhile return\(\mathrm{VNR_{c}[]}\)
```  
**Algorithm 1** Wii classification mapping algorithm of VNRs  
Figure 4 depicts a flowchart representation of the algorithmic steps of the Wii classification process. Alternatively, orchestration mechanisms can be trained to acknowledge the discerned patterns and optimally evaluate SN resources for path mappings. Automated VNR accommodation based on the requirements of intricate NGN services which demand comprehensive performance optimization approaches can be a goal for future work. Figure 4: Flowchart representation of Wii classification algorithm.