-In the NAS benchmarks there are some programs executed on different number of
-nodes. The benchmarks CG, MG, LU and FT executed on 2 to a power of (1, 2, 4, 8,
-\dots{}) of nodes. The other benchmarks such as BT and SP executed on 2 to a
-power of (1, 2, 4, 9, \dots{}) of nodes. We are take the average of energy
-saving, performance degradation and distances for all results of NAS
-benchmarks. The average of these three objectives are plotted to the number of
-nodes as in plots (\ref{fig:avg_eq} and \ref{fig:avg_neq}). In CG, MG, LU, and
-FT benchmarks the average of energy saving is decreased when the number of nodes
-is increased due to the increasing in the communication times as mentioned
-before. Thus, the average of distances (our objective function) is decreased
-linearly with energy saving while keeping the average of performance degradation
-the same. In BT and SP benchmarks, the average of energy saving is not decreased
-significantly compare to other benchmarks when the number of nodes is
-increased. Nevertheless, the average of performance degradation approximately
-still the same ratio. This difference is depends on the characteristics of the
-benchmarks such as the computation to communication ratio that has.
-
-\subsection{The results for different powers scenarios}
-
-The results of the previous section are obtained using a percentage of 80\% for
-dynamic power and 20\% for static power of total power consumption. In this
-section we are change these ratio by using two others scenarios. Because is
-interested to measure the ability of the proposed algorithm to changes it
-behavior when these power ratios are changed. In fact, we are use two different
-scenarios for dynamic and static power ratios in addition to the previous
-scenario in section (\ref{sec.res}). Therefore, we have three different
-scenarios for three different dynamic and static power ratios refer to as:
-70\%-20\%, 80\%-20\% and 90\%-10\% scenario. The results of these scenarios
-running NAS benchmarks class C on 8 or 9 nodes are place in the tables
-(\ref{table:res_s1} and \ref{table:res_s2}).
+Figures \ref{fig:energy} and \ref{fig:per_deg} present the energy saving and
+performance degradation respectively for all the benchmarks according to the
+number of used nodes. As shown in the first plot, the energy saving percentages
+of the benchmarks MG, LU, BT and FT decrease linearly when the number of nodes
+increase. While for the EP and SP benchmarks, the energy saving percentage is
+not affected by the increase of the number of computing nodes, because in these
+benchmarks there are little or no communications. Finally, the energy saving of
+the GC benchmark significantly decrease when the number of nodes increase
+because this benchmark has more communications than the others. The second plot
+shows that the performance degradation percentages of most of the benchmarks
+decrease when they run on a big number of nodes because they spend more time
+communicating than computing, thus, scaling down the frequencies of some nodes
+has less effect on the performance.
+
+
+
+
+\subsection{The results for different power consumption scenarios}
+\label{sec.compare}
+The results of the previous section were obtained while using processors that
+consume during computation an overall power which is 80\% composed of dynamic
+power and of 20\% of static power. In this section, these ratios are changed and
+two new power scenarios are considered in order to evaluate how the proposed
+algorithm adapts itself according to the static and dynamic power values. The
+two new power scenarios are the following:
+
+\begin{itemize}
+\item 70\% of dynamic power and 30\% of static power
+\item 90\% of dynamic power and 10\% of static power
+\end{itemize}
+
+The NAS parallel benchmarks were executed again over processors that follow the
+new power scenarios. The class C of each benchmark was run over 8 or 9 nodes
+and the results are presented in Tables \ref{table:res_s1} and
+\ref{table:res_s2}. These tables show that the energy saving percentage of the
+70\%-30\% scenario is smaller for all benchmarks compared to the energy saving
+of the 90\%-10\% scenario. Indeed, in the latter more dynamic power is consumed
+when nodes are running on their maximum frequencies, thus, scaling down the
+frequency of the nodes results in higher energy savings than in the 70\%-30\%
+scenario. On the other hand, the performance degradation percentage is smaller
+in the 70\%-30\% scenario compared to the 90\%-10\% scenario. This is due to the
+higher static power percentage in the first scenario which makes it more
+relevant in the overall consumed energy. Indeed, the static energy is related
+to the execution time and if the performance is degraded the amount of consumed
+static energy directly increas. Therefore, the proposed algorithm does not
+really significantly scale down much the frequencies of the nodes in order to
+limit the increase of the execution time and thus limiting the effect of the
+consumed static energy.
+
+Both new power scenarios are compared to the old one in figure
+(\ref{fig:sen_comp}). It shows the average of the performance degradation, the
+energy saving and the distances for all NAS benchmarks of class C running on 8
+or 9 nodes. The comparison shows that the energy saving ratio is proportional
+to the dynamic power ratio: it is increased when applying the 90\%-10\% scenario
+because at maximum frequency the dynamic energy is the most relevant in the
+overall consumed energy and can be reduced by lowering the frequency of some
+processors. On the other hand, the energy saving decreases when the 70\%-30\%
+scenario is used because the dynamic energy is less relevant in the overall
+consumed energy and lowering the frequency does not return big energy savings.
+Moreover, the average of the performance degradation is decreased when using a
+higher ratio for static power (e.g. 70\%-30\% scenario and 80\%-20\%
+scenario). Since the proposed algorithm optimizes the energy consumption when
+using a higher ratio for dynamic power the algorithm selects bigger frequency
+scaling factors that result in more energy saving but less performance, for
+example see Figure (\ref{fig:scales_comp}). The opposite happens when using a
+higher ratio for static power, the algorithm proportionally selects smaller
+scaling values which result in less energy saving but also less performance
+degradation.
+
