adding the conclution

diff --git a/Heter_paper.tex b/Heter_paper.tex
index a6d44b15e409c0d23745a320d0d950e0663d5eef..6f2bd7abdb194e74b1d0d7bb94e5aca6d77a6105 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -1040,7 +1040,7 @@ linearly related the execution time and the dynamic energy is related to the com
 the work presented in this paper is based on the execution time model. To verify this model, the predicted 
 execution time was compared to  the real execution time over Simgrid for all  the NAS parallel benchmarks 
 running class B on 8 or 9 nodes. The comparison showed that the proposed execution time model is very precise, 
 the work presented in this paper is based on the execution time model. To verify this model, the predicted 
 execution time was compared to  the real execution time over Simgrid for all  the NAS parallel benchmarks 
 running class B on 8 or 9 nodes. The comparison showed that the proposed execution time model is very precise, 

-the maximum normalized difference between  the predicted execution time  and the real execution time is equal 
+the maximum normalized difference between the predicted execution time  and the real execution time is equal 

 to 0.03 for all the NAS benchmarks.
 
 Since  the proposed algorithm is not an exact method and do not test all the possible solutions (vectors of scaling factors) 
 to 0.03 for all the NAS benchmarks.
 
 Since  the proposed algorithm is not an exact method and do not test all the possible solutions (vectors of scaling factors) 


@@ -1052,14 +1052,14 @@ for a heterogeneous cluster composed of four different types of nodes having the
 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 $O(F\cdot (N \cdot4) )$, 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 
 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 $O(F\cdot (N \cdot4) )$, 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 sections (\ref{sec.res}) and (\ref{sec.compare}) .
+vector of frequency scaling factors that gives the results of the sections (\ref{sec.res}) and (\ref{sec.compare}).

 
 \section{Conclusion}
 \label{sec.concl}
 In this paper, we have presented a new online heterogeneous scaling algorithm
 that selects the best possible vector of frequency scaling factors. This vector 
 
 \section{Conclusion}
 \label{sec.concl}
 In this paper, we have presented a new online heterogeneous scaling algorithm
 that selects the best possible vector of frequency scaling factors. This vector 

-gives the maximum distance (optimal tradeoff) between the normalized energy and 
-the performance curves. In addition, we developed a new energy model for measuring  
+gives the maximum distance (optimal tradeoff) between the predicted energy and 
+the predicted performance curves. In addition, we developed a new energy model for measuring  

 and predicting the energy of distributed iterative applications running over heterogeneous 
 cluster. The proposed method evaluated on Simgrid/SMPI  simulator to built a heterogeneous 
 platform to executes NAS parallel benchmarks. The results of the experiments showed the ability of
 and predicting the energy of distributed iterative applications running over heterogeneous 
 cluster. The proposed method evaluated on Simgrid/SMPI  simulator to built a heterogeneous 
 platform to executes NAS parallel benchmarks. The results of the experiments showed the ability of