Correct definition for F in algorithmic complexity.

diff --git a/Heter_paper.tex b/Heter_paper.tex
index 55d72f68e82e32c3bee332d3a9fa835b62bff22c..a9a1e6da497ddbd13099d0c00c389ec2b6a52213 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -731,9 +731,9 @@ cluster composed of four different types of nodes having the characteristics
 presented in Table~\ref{table:platform}, it takes on average \np[ms]{0.04} for 4
 nodes and \np[ms]{0.15} on average for 144 nodes to compute the best scaling
 factors vector.  The algorithm complexity is $O(F\cdot N)$, where $F$ is the
 presented in Table~\ref{table:platform}, it takes on average \np[ms]{0.04} for 4
 nodes and \np[ms]{0.15} on average for 144 nodes to compute the best scaling
 factors vector.  The algorithm complexity is $O(F\cdot N)$, where $F$ is the

-number of iterations and $N$ is the number of computing nodes. The algorithm
-needs from 12 to 20 iterations to select the best vector of frequency scaling
-factors that gives the results of the next sections.
+maximum number of available frequencies, and $N$ is the number of computing
+nodes. The algorithm needs from 12 to 20 iterations to select the best vector of
+frequency scaling factors that gives the results of the next sections.

 
 \begin{table}[!t]
   \caption{Heterogeneous nodes characteristics}
 
 \begin{table}[!t]
   \caption{Heterogeneous nodes characteristics}