## 6 Experimental Evaluation
### Experimental Evaluation and Discussion  
The process of deriving a Pareto front starts by selecting all available Fog storage nodes that meet the minimal objectives' criteria, requested by the user. Once the nodes that meet the minimal requirements are selected, they are pair-wise compared and a non-dominated sorting is performed. The result provides a subset of optimal Fog storage nodes that have optimal balance in relation to the three conflicting objectives. Hence, the user is offered a reduced number of storage nodes, which are closest to the chosen requirements.  
To prove the feasibility of the described decision-making method, the Pareto front is evaluated in two separate cases: (1) with randomly generated set of 100 nodes with simulated metrics and (2) with a set of 13 storage nodes with real-time metrics (see Table 2). All available storage nodes are situated in the countries within the European Union.  
The metrics for the 13 real storage nodes were gathered from Slovenia in a period of two months prior to the evaluation process.  
The Pareto front is derived on the basis of the SLA parameters that are described in Section 4.4. Essentially, the Pareto front is built based on the trade-offs between storage availability, network throughput and cost as conflicting objectives. The result of the decision-making process for the simulated environment is illustrated in Figure 4. From the plots, it is noticeable that in the moment when the user has to choose a storage from 100 available storage nodes, the Pareto front significantly narrows down the choice to the non-dominated nodes by removing up to 85% of the nodes that do not belong to the Pareto front.  
Similar to the simulated environment, the Pareto-based decision-making mechanism successfully derived the optimal nodes among the pool of available Fog nodes in the DE  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline \multirow{2}{*}{**Infrastructure**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Availability**} & \multirow{2}{*}{**Throughput**} & \multirow{2}{*}{**Cost [s]**} \\  & & **[\%]** & & **[Mb/s]** \\ \hline aws-eu-west-3 & France & 99.999 & 12.1 & 0.306 \\ aws-eu-south-1 & Italy & 99.999 & 5.41 & 0.297 \\ aws-eu-central-1 & Germany & 99.999 & 11.81 & 0.237 \\ \hline gck-eu-west-3 & Germany & 99.999 & 21.35 & 0.200 \\ gck-eu-west-6 & Switzerland & 99.999 & 20.73 & 0.221 \\ \hline azr-de-central & Germany & 99.95 & 18.22 & 0.021 \\ azr-eu-west & The Netherlands & 99.95 & 19.62 & 0.020 \\ \hline si-node-0 & & 89.0 & 12.01 & 0.001 \\ si-node-1 & & 89.0 & 17.99 & 0.015 \\ si-node-2 & & 90.0 & 10.88 & 0.050 \\ si-node-3 & & 90.0 & 20.49 & 0.022 \\ si-node-4 & & 90.0 & 15.1 & 0.001 \\ si-node-5 & & 90.0 & 12.3 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental testbed infrastructures with metrics measured from Slovenia.  
Figure 4: Pareto front derived from the experimental evaluation with simulated input data. The plot on the left illustrates all available Fog storage nodes, whereas the plot on the right illustrates only the non-dominated nodes that form the Pareto front.  
CENTER Fog and Brokerage Platform, where it narrowed the choice of optimal Fog storage nodes to five possible non-dominated solutions, as illustrated in Figure 5. Upon deriving the Pareto-optimal storage nodes, the user has a significantly easier task selecting the most desired storage infrastructure, which is done by performing a trade-off between the SLOs as illustrated in Figure 6.  
In summary, the proposed decision-making method successfully derived optimal storage nodes by utilising the newly proposed SLA specification that consisted of the chosen input parameters for the decision-making process. The proposed method successfully highlights the optimal storage nodes and reduces the number of possible choices to only a few, hence simplifying the selection process for the service user.