Figure \ref{fig:time_sen} presents the execution times for all the benchmarks over the two scenarios. For most of the benchmarks running over the one site scenario, their execution times are approximately divided by two when the number of computing nodes is doubled from 16 to 32 nodes (linear speed up according to the number of the nodes).
+\textcolor{red}{The transition between the execution times to the performance degradation is not clear}
-\textcolor{blue}{
-The performance degradation percentage of EP benchmark is the higher when it is compared with
-the other benchmarks. There are no communication and slack times in this benchmark and its
-performance degradation percentage depends on the frequency value selected in the computing node.
-The rest of the benchmarks showed different performance degradation percentages, which are decreased
-when the communication times are increased and vice versa.}
+The performance degradation percentage of the EP benchmark after applying the scaling factors selection algorithm is the highest in comparison to
+the other benchmarks. Indeed, in the EP benchmark, there are no communication and slack times and its
+performance degradation percentage only depends on the frequencies values selected by the algorithm for the computing nodes.
+The rest of the benchmarks showed different performance degradation percentages, which decrease
+when the communication times increase and vice versa.
-\textcolor{blue}{Figure \ref{fig:dist} presents the tradeoff distance percentage between the energy saving and the performance degradation for all benchmarks over both scenarios. The tradeoff distance percentage can be
-computed as in the tradeoff function \ref{eq:max}. The one site scenario with 16 nodes gives the best energy and performance
-tradeoff, on average is equal to 26\%. As a result, one site scenario using both 16 and 32 nodes had better energy and performance
-tradeoff comparing to the two sites scenario. This because the former used high speed local communications
-which increased the computations to communications ratio and the latter used long distance communications which decreased this ratio. } \textcolor{red}{The last paragraph has compared the two scenarios}
+Figure \ref{fig:dist} presents the distance percentage between the energy saving and the performance degradation for each benchmark over both scenarios. The tradeoff distance percentage can be
+computed as in equation \ref{eq:max}. The one site scenario with 16 nodes gives the best energy and performance
+tradeoff, on average it is equal to 26\%. The one site scenario using both 16 and 32 nodes had better energy and performance
+tradeoff comparing to the two sites scenario because the former has high speed local communications
+which increase the computations to communications ratio and the latter uses long distance communications which decrease this ratio.
Finally, the best energy and performance tradeoff depends on all of the following:
The execution times for most of the NAS benchmarks are higher over the one site multi-cores per node scenario
than the execution time of those running over one site single core per node scenario. Indeed,
the communication times are higher in the one site multi-cores scenario than in the latter scenario because all the cores of a node share the same node network link which can be saturated when running communication bound applications. On the other hand, the execution times for most of the NAS benchmarks are lower over
-the two sites multi-cores scenario than those over the two sites one core scenario.
+the two sites multi-cores scenario than those over the two sites one core scenario. In the two sites multi-cores scenario, There are three types of communications :
+\begin{itemize}
+\item between cores on the same node via shared memory
+\item between cores from distinct nodes but belonging to the same cluster or site via local network
+\item between cores from distinct sites via long distance network
+\end{itemize}
+The latency of the communications increases from shared memory to LAN to WAN.
+Therefore, using multi-cores communicating via shared memory
+has reduced the communication times, and thus the overall execution time is also decreased.
+
-\textcolor{blue}{Furthermore, in two sites multi-cores per node scenario part of the communications happened via shared memory
-and the rest via long distance network. According to the high latency in the long distance network, the
-communication times are smaller compared to the communication times of the shared memory.
-Therefore, using the shared memory communications mixed with the long distance communications
-has decreased the communication times, and thus the overall execution time is decreased.}
The experiments showed that for most of the NAS benchmarks and between the four scenarios,
the one site one core scenario gives the best execution times because the communication times are the lowest.
one site one core scenario than over the one site multi-cores scenario because
the first scenario had less execution time than the latter which results in less static energy being consumed.
-\textcolor{blue}{
-Therefore, the computations to communications ratios of the NAS benchmarks are higher over
-the one site one core scenario compared to the other scenarios.
-More energy reduction has achieved when this ratio increased, because the proposed scaling algorithm selecting smaller frequencies that decreased the dynamic power consumption. Whereas, the energy consumption in the two sites multi-cores scenario is higher than the energy consumption
-of the two sites one core scenario. Actually, using multi-cores in this scenario decreased the communication times that decreased the static energy consumption.}
+The computations to communications ratios of the NAS benchmarks are higher over
+the one site one core scenario when compared to the ratios of the other scenarios.
+More energy reduction was achieved when this ratio is increased because the proposed scaling algorithm selects smaller frequencies that decrease the dynamic power consumption.
+
+
+ \textcolor{red}{ The next sentence is completely false! It is impossible to have these results! Whereas, the energy consumption in the two sites multi-cores scenario is higher than the energy consumption
+of the two sites one core scenario.
+Actually, using multi-cores in this scenario decreased the communication times that decreased the static energy consumption.}
These experiments also showed that the energy
which could increase or decrease the static power consumptions. Contrary to EP and MG, the energy consumptions
and the execution times of the rest of the benchmarks vary according to the communication times that are different from one scenario to the other.
-\textcolor{blue}{
-The energy saving percentages of all NAS benchmarks running over these four scenarios are presented in the figure \ref{fig:eng-s-mc}. This figure
-shows that the energy saving percentages are higher over the two sites multi-cores scenario
-than over the two sites one core scenario, on average they are equal to 22\% and 18\%
-respectively. This is according to the increase or decrease in the computations to communications ratio as mentioned previously.}
+
+The energy saving percentages of all NAS benchmarks running over these four scenarios are presented in the figure \ref{fig:eng-s-mc}. It shows that the energy saving percentages over the two sites multi-cores scenario
+and over the two sites one core scenario are on average equal to 22\% and 18\%
+respectively. The energy saving percentages are higher in the former scenario because its computations to communications ratio is higher than the ratio of the latter scenario as mentioned previously.
In contrast, in the one site one
the proposed scaling algorithm to select similar frequencies for both scenarios.
The performance degradation percentages of the NAS benchmarks are presented in
-figure \ref{fig:per-d-mc}.
-
-It indicates that the performance degradation percentages for the NAS benchmarks are higher over the two sites
+figure \ref{fig:per-d-mc}. It shows that the performance degradation percentages for the NAS benchmarks are higher over the two sites
multi-cores scenario than over the two sites one core scenario, equal on average to 7\% and 4\% respectively.
Moreover, using the two sites multi-cores scenario increased
the computations to communications ratio, which may increase
the overall execution time when the proposed scaling algorithm is applied and the frequencies scaled down.
-\textcolor{blue}{
+
When the benchmarks are executed over the one
-site one core scenario their performance degradation percentages, on average
-is equal to 10\%, are higher than those executed over one site multi-cores,
-which on average is equal to 7\%. This because using multi-cores in one site scenario
-decreased the computations to communications ratio. Therefore, selecting small
-frequencies by the scaling algorithm do not increase the execution time significantly.}
+site one core scenario, their performance degradation percentages are equal on average
+to 10\% and are higher than those executed over the one site multi-cores scenario,
+which on average is equal to 7\%.
+
+\textcolor{red}{the next sentence is completely false!
+The higher performance degradation percentages over the first scenario is due to the use of multi-cores which
+decreases the computations to communications ratio. Therefore, selecting small
+frequencies by the scaling algorithm do not increase the execution time significantly. }
+
-\textcolor{blue}{
The tradeoff distance percentages of the NAS
benchmarks over all scenarios are presented in the figure \ref{fig:dist-mc}.
-These tradeoff distance percentages are used to verified which scenario is the best in term of the energy and performance ratio. The figure indicates that using muti-cores in both of the one site and two sites scenarios gives bigger tradeoff distance percentages, on overage they are equal to 17.6\% and 15.3\% respectively. On the contrary, using one core per node in both of one site and two sites scenarios gives lower tradeoff distance percentages, on average they are equal to 14.7\% and 13.3\% respectively. }
+These tradeoff distance percentages are used to verify which scenario is the best in terms of energy reduction and performance. The figure shows that using muti-cores in both of the one site and two sites scenarios gives bigger tradeoff distance percentages, on overage equal to 17.6\% and 15.3\% respectively, than using one core per node in both of one site and two sites scenarios, on average equal to 14.7\% and 13.3\% respectively.
\begin{table}[]
\centering
