\label{fig:dist-mc}
\end{figure}
-\subsection{The results of using different static power consumption scenarios}
+\subsection{Experiments with different static and dynamic powers consumption scenarios}
\label{sec.pow_sen}
-\textcolor{blue}{
-The static power consumption for one core is the leakage power
-consumption when it is idle. The measured static power of the node,
-as in section \ref{sec.grid5000}, had a collection of power values such as
-all cores static powers and the power consumptions of the other devices. Furthermore, the static power for one core is hard to measured precisely. On the other hand, the core has consumed the static power during
-the communication and computation times. However, the static power consumption becomes more important when the execution time is
-increased using DVFS. Therefore, the objective of this section is to verify the ability of the proposed
-scaling algorithm to select the best frequencies when the static power consumption is changing.
-All the results obtained in the previous sections depend on the measured dynamic power
-consumptions as in table \ref{table:grid5000}. Moreover, the static power consumption for one core is represented by 20\% of the measured dynamic power consumption.
-This assumption is extended in this section to taking into account other ratios for the static power consumption.
-In addition to the previous ratio of the static power consumption, two other static power ratios are used, which are 10\% and 30\% of the measured dynamic power of the core.
-As a result, all of these static power scenarios is denoted as follow:
-\begin{itemize}
-\item 10\% of static power scenario
-\item 20\% of static power scenario
-\item 30\% of static power scenario
-\end{itemize}
-The NAS parallel benchmarks, class D, are executed over Nancy site.
-The number of computing nodes used is 16 nodes distributed between three cluster, which are Graphite, Graphene and Griffon. The NAS benchmarks rerun
-with these two new static power scenarios over one site scenario
-using one core per node. }
+
+In section \ref{sec.grid5000}, since it was not possible to measure the static power consumed by a CPU, the static power was assumed to be equal to 20\% of the measured dynamic power. This power is consumed during the whole execution time, during computation and communication times. Therefore, when the DVFS operations are applied by the scaling algorithm and the CPUs' frequencies lowered, the execution time might increase and consequently the consumed static energy will be increased too.
+
+The aim of this section is to evaluate the scaling algorithm while assuming different values of static powers.
+In addition to the previously used percentage of static power, two new static power ratios, 10\% and 30\% of the measured dynamic power of the core, are used in this section.
+The experiments have been executed with these two new static power scenarios and over the one site one core per node scenario.
+In these experiments, the class D of the NAS parallel benchmarks are executed over Nancy's site. 16 computing nodes from the three sites, Graphite, Graphene and Griffon, where used in this experiment.
\begin{figure}
\centering
\label{fig:fre-pow}
\end{figure}
-\textcolor{blue}{
-The energy saving percentages of NAS benchmarks with these three static power scenarios are presented
-in figure \ref{fig:eng_sen}. This figure shows that 10\% of static power scenario
-gives the biggest energy saving percentage comparing to 20\% and 30\% static power
-scenarios. The smaller ratio of the static power consumption makes the proposed
-scaling algorithm to select smaller frequencies, bigger scaling factors.
-These smaller frequencies has reduced the dynamic energy consumption and thus the
-overall energy consumption is decreased.
-The energy saving percentages of 30\% static power scenario is the smallest between the other scenarios, because of the scaling algorithm selects bigger frequencies, smaller scaling factors, that increased the energy consumption. For example, figure \ref{fig:fre-pow}, illustrates that the proposed scaling algorithm is proportionally selected the best frequency scaling factors according to the static power consumption ratio being used.
+
+The energy saving percentages of the NAS benchmarks with the three static power scenarios are presented
+in figure \ref{fig:eng_sen}. This figure shows that the 10\% of static power scenario
+gives the biggest energy saving percentage in comparison to the 20\% and 30\% static power
+scenarios. The small value of static power consumption makes the proposed
+scaling algorithm select smaller frequencies for the CPUs.
+These smaller frequencies reduce the dynamic energy consumption more than increasing the consumed static energy which gives less overall energy consumption.
+The energy saving percentages of the 30\% static power scenario is the smallest between the other scenarios, because the scaling algorithm selects bigger frequencies for the CPUs which increases the energy consumption. Figure \ref{fig:fre-pow} demonstrates that the proposed scaling algorithm selects the best frequency scaling factors according to the static power consumption ratio being used.
+
+\textcolor{red}{ The following paragraph is not clear at all. Please rewrite.
Furthermore, the proposed scaling algorithm tries to limit selecting smaller frequencies, which increased the execution time. Hence, the increase in the execution time is relatively increased the static energy consumption.
The performance degradation percentages are presented in the figure \ref{fig:per-pow},
the 30\% of static power scenario had less performance degradation percentage. This because
