## 4 Integrative Example
### Methodology of Testing and Results  
To validate our approach, we created a system design of the interpreter, constituted by the properties and the values in the example as well as their pertaining logic. The simulator we chose to model our system is Insight Maker, a \"general-purpose web-based simulation and modeling tool\" [37]. We chose Insight Maker due to its low learning curve, high flexibility, collaborative features, support of system dynamics as well as agent-based modeling and, last but not least, free and open-source nature. With it, we portrayed each of the elements of the BBCP as variables, and the underlying logic to interpret them as separate state machines. The Catalog Service and Game Stream Service models created with Insight Maker are accessible through our website [33]. For clarity, we decided to create a separate model for each service. As the Game Stream Service maintains an operational loop, a simulation that uses the accumulated run time of the Catalog Service as a direct input for the Game Stream Service to use as the operational time was enough. The equations and hardware specifications to obtain the energy consumption of the service were taken from various \begin{table}
\begin{tabular}{c c c} \hline \hline
**Resource usage** & **Operation 2** & **Operation 3** \\ \hline CPU (GHz) & \(\sim\)0.66 & \(\sim\)1.33 \\ \hline RAM (GB) & \(\sim\)0.021 & \(\sim\)0.042 \\ \hline Storage & N/A & N/A \\ \hline Network (Mbit) & \(\sim\)8.33 & \(\sim\)16.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Game Stream Service: resource usage per operation. Figure 7: Sequence diagram showing the interactions between the Catalog Service and the Game Stream Service. sources, such as Intel datasheets and official network device standards. The equations employed per hardware resource are available in Table 11, and the variables and values we used are available in Table 12.  
We performed three experiments: one that sampled the energy consumption of the first cycle of the game stream profile for a day and a second one that sampled the energy consumption of its second cycle for a day. In the third experiment, we sampled an hour of the second experiment during its third definition of timed expectations, using an agent simulation of 50 agents. In the context of Insight Maker and our interpreter, an agent is a dynamic representation of a BBCP; in other words, 50 instances of a BBCP were used. The steps we followed in executing our first two experiments were the following:  
1. We executed a simulation of the first cycle of the Catalog Service with a duration of a day. 2. We executed a simulation of the first cycle of the Game Stream Service using the accumulated run time of our first experiment as an input, sampling the behavior over a single day. 3. We repeated the previous steps using the data of the second cycle for each profile. Our two criteria for experimental success were the following:  
1. A quantitative approximation of the energy consumption should be obtained to demonstrate that energy consumption can be drawn from the simulation of behavioral profiles. 2. Both of the cycles defined above should be simulated and contrasted to understand how the re-definition of behavior affects the consumption of hardware and, in the end, energy consumption. By using our BBCP and the test engine in Insight Maker, we estimated an energy consumption of 127,700.10 Joules over 14,403 s generated by the Game Stream Service \begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware Device** & **Equations** \\ \hline CPU & \(CV^{2}FT\)[38] \\ \hline RAM & \(CT\) \\ \hline Storage & N/A \\ \hline Network & \((W_{u}\frac{L}{Lmax}+W_{d}\frac{Lmax-L}{Lmax})T\)[39] \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Equations used to estimate the energy consumption of the hypothetical hardware usage of the BBCP.