-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}).
+Plots (\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 are decreased linearly when the the
+number of nodes is increased. 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 is significantly decreased when the number
+of nodes is increased because this benchmark has more communications than the others. The second plot
+shows that the performance degradation percentages of most of the benchmarks are decreased 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 have 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 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\% dynamic power and 30\% static power
+\item 90\% dynamic power and 10\% static power
+\end{itemize}
+
+The NAS parallel benchmarks were executed again over processors that follow the 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 less 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 less 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 total consumed
+static energy is directly increased. Therefore, the proposed algorithm do not scales 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 .
+
+The two 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 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 is decreased when the 70\%-30\% scenario is used because the dynamic energy is less relevant in
+the overall consumed energy and lowering the frequency do not returns 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 the figure (\ref{fig:scales_comp}). The opposite happens
+when using a higher ratio for static power, the algorithm proportionally selects smaller scaling values which
+results in less energy saving but less performance degradation.
+
