## 5 Experiments
### Experiment 2: Static Splitting Factor with No Stopping Criteria  
The _purpose_ of this experiment is to evaluate the efficiency of the splitting strategy for different splitting factors, calculating the _time elapsed_ and _resources consumption_. These correspond to the first and third metrics of Section 3.3. More specifically, in each epoch, each node is split into replica nodes and the connections serviced by the original node are redistributed equally among the new ones. In this experiment, we evaluate each case with different pools of initial connections, considering no stopping criteria. Note at this point that recycling replicas or other techniques for conserving resources have not been considered. The results of this experiment may allow us to properly calibrate the stopping criteria for the subsequent simulations. By examining the results of the simulations, we observe that low-splitting steps generally lead to more efficient solutions. With reference to Figure 4 and Table 3, considering a pollution factor of 1% and a small-sized connection pool of 1K to 4K, then splitting factors of 2 and 4 are optimal. For medium-sized connection pools, i.e., 5K to 7K, a splitting factor of 6 provides optimal results, while for large-sized pools, namely, 8K to 10K connections, the optimal splitting step is 10. Low splitting factors generally lead to the spawning of a lower total number of replicas. This translates as follows. For 2K connections and a pollution factor of 1% using a splitting factor of 4 will lead to the full solution in the shortest time; however, a total of 126 replicas need to be spawned. In contrast, using an even smaller splitting factor of 2 will require even fewer replicas, i.e., 100, meaning 26% less replica utilization. For the same pollution factor, assuming a 10K pool of connections and the optimal split factor in terms of time efficiency, i.e., 10, then a total of 901 replicas will be spawned as opposed to 440 when the splitting factor is 2. This is an increase of roughly 50%. In this respect, it is clear that there is a trade-off between time and resource requirements, depending on the employed splitting factor. Oppositely, higher splitting factors lead to less effective solutions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Time (min)** & \multicolumn{4}{c}{**Num. of Replicas**} \\ \hline
**Num. of Conn.** & **Par. Approach** & **Ser. Approach** & **Num. of Conn.** & **Par. Approach** & **Ser. Approach** \\ \hline
1000 & 0.18 & 83.52 & 1000 & 1000 & 1 \\
2000 & 0.27 & 167.10 & 2000 & 2000 & 1 \\
3000 & 0.37 & 250.55 & 3000 & 3000 & 1 \\
4000 & 0.45 & 334.12 & 4000 & 4000 & 1 \\
5000 & 0.54 & 417.37 & 5000 & 5000 & 1 \\
6000 & 0.64 & 501.28 & 6000 & 6000 & 1 \\
7000 & 0.73 & 584.64 & 7000 & 7000 & 1 \\
8000 & 0.82 & 668.07 & 8000 & 8000 & 1 \\
9000 & 0.91 & 751.45 & 9000 & 9000 & 1 \\
10,000 & 1.00 & 835.20 & 10,000 & 10,000 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time elapsed in minutes (left) and resource consumption (right). Figure 3: Comparison between the two extreme approaches based on the average time (**left**) and the total number of replicas required (**right**) without any stopping criterion applied. In this example, a pollution rate of 5% is considered. Specifically, in small-sized connection pools, splitting factors of 14 and 20 yield the poorest performance, whereas in medium- to large-sized connection pools, factors of 16, 18, and 20 exhibit inferior performance. Indicatively, when considering 10K connections, a splitting factor of 20 requires around 28 min and 1000 nodes to reach a complete solution, meaning that there is a 65.6% and a 58.5% increase for time and resource demands, respectively, compared to the best-performing factors. Similarly, as depicted in Figure 4 and outlined in Tables 4 and 5, when applying a pollution factor of 5% and 10%, low-to-medium splitting factors outperform the high ones both in time and resource requirements.