# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 4 Experimental Setup and Scenario-Based Simulation
### USL Model Generation Based on Concurrency and Throughput/Response Time
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**1. Serial \#** & \begin{tabular}{c} **2. Workload** \\ **length** \\ **(MIPS)** \\ \end{tabular} & \begin{tabular}{c} **3. Response** \\ **Time (sec)** \\ \end{tabular} & \begin{tabular}{c} **4. Throughput** \\ **(kbps)** \\ \end{tabular} & \begin{tabular}{c} **5. Workload** \\ **Distribution** \\ **(\%age)** \\ \end{tabular} & \
\begin{tabular}{c} **6. ANFIS** \\ **Decision Rules** \\ \end{tabular} &
\begin{tabular}{c} **7. Decision Rule** \\ **Distribution** \\ **Percentile (\%age)** \\ \end{tabular} \\ \hline
1 & 28,000 & 0.209 & 61.261 & 15\% &
\begin{tabular}{c} Definitely No \\ Violation \\ (DNV) \\ \end{tabular} & 5\% \\
2 & 32,000 & 0.216 & 55.278 & 40\% &
\begin{tabular}{c} Definitely No \\ Violation \\ (DNV) \\ \end{tabular} & 80\% \\
3 & 35,200 & 0.220 & 51.136 & 35\% & Probably \\
4 & 36,000 & 0.221 & 50.227 & 10\% &
\begin{tabular}{c} Probably \\ Violation \\ (PV) \\ \end{tabular} & 14\% \\
5 & 64,800 & 0.240 & 31.612 & 20\% &
\begin{tabular}{c} \\ (DNV) \\ \end{tabular} & 1\% \\
6 & 123,600 & 0.267 & 18.043 & 15\% &
\begin{tabular}{c} Definitely \\ Violation \\ (DV) \\ \end{tabular} & 8\% \\
7 & 205,600 & 0.322 & 12.960 & 40\% &
\begin{tabular}{c} Definitely \\ Violation \\ (DV) \\ \end{tabular} & 80\% \\
8 & 264,000 & 0.374 & 11.607 & 10\% &
\begin{tabular}{c} \\ (DNV) \\ \end{tabular} & 10\% \\
9 & 278,800 & 0.407 & 10.843 & 15\% &
\begin{tabular}{c} Probably \\ Violation \\ (PV) \\ \end{tabular} & 10\% \\
10 & 296,800 & 0.534 & 7.962 & 35\% & Probably \\
11 & 302,400 & 0.571 & 7.166 & 30\% &
\begin{tabular}{c} Probably \\ Violation \\ (PV) \\ \end{tabular} & 14\% \\
12 & 312,400 & 0.663 & 6.235 & 20\% &
\begin{tabular}{c} \\ (PV) \\ \end{tabular} & 13\% \\
13 & 317,200 & 0.782 & 5.808 & 15\% &
\begin{tabular}{c} Definitely \\ Violation \\ (DV) \\ \end{tabular} & 14\% \\
14 & 318,400 & 0.916 & 5.412 & 30\% &
\begin{tabular}{c} Definitely \\ Violation \\ (DV) \\ \end{tabular} & 1\% \\
15 & 319,200 & 1.004 & 5.185 & 35\% &
\begin{tabular}{c} \\ (DV) \\ \end{tabular} & 1\% \\
16 & 320,000 & 1.115 & 4.958 & 20\% & \
\begin{tabular}{c} \\ (DV) \\ \end{tabular} & 20\% &
\begin{tabular}{c} \\ (DV) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: QoS Metrics, Workload with Decision Rules. able coefficients. To accomplish this, it needs a collection of workload or size measurements (usually concurrency or node count) of the system and the corresponding throughput. Then the USL model is fitted to the dataset, using non-linear least squares regression. A mathematical technique determines the optimal coefficient values to determine the best fit line across the measurements. The product is the values of \(\lambda\), \(\sigma\), and \(\kappa\). By visualizing the data in scatterplot and time series formats, it has cleaned and curated the data and maintained a reasonably consistent data collection. Individual points are eliminated or modified within the timeframe needed to experiment with data and averaged over time to achieve satisfactory results.