text for curves at the end of sction 5

diff --git a/Heter_paper.tex b/Heter_paper.tex
index 11e947556696bb90494252f67fc520a413b10656..e9f6b67dc67f73de52ea6f73b85ccacfe72da6ba 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -352,10 +352,19 @@ nodes having the characteristics presented in table~(\ref{table:platform}), it
 takes \np[ms]{0.04} on average for 4 nodes and \np[ms]{0.15} on average for 144
 nodes.  The algorithm complexity is $O(F\cdot (N \cdot4) )$, where $F$ is the
 number of iterations and $N$ is the number of computing nodes. The algorithm
 takes \np[ms]{0.04} on average for 4 nodes and \np[ms]{0.15} on average for 144
 nodes.  The algorithm complexity is $O(F\cdot (N \cdot4) )$, where $F$ is the
 number of iterations and $N$ is the number of computing nodes. The algorithm

-needs on average from 12 to 20 iterations to selects the best vector of frequency scaling factors that give the results of the next section. \textbf{put the lst paragraph in experiments}
-
+needs on average from 12 to 20 iterations to selects the best vector of frequency scaling factors that give the results of the next section. 

 
 
 
 

+Therefore, there is a small distance between the energy and
+the performance curves in a homogeneous cluster compare to heterogeneous one, for example see the figure(\ref{fig:r1}) and figure(\ref{fig:r2}) .  Then the
+algorithm starts to search for the optimal vector of the frequency scaling factors from the selected initial 
+frequencies until all node reach their minimum frequencies.
+\begin{figure}[t]
+  \centering
+    \includegraphics[scale=0.5]{fig/start_freq}
+  \caption{Selecting the initial frequencies}
+  \label{fig:st_freq}
+\end{figure}